Climate Change Belief Analysis - 2201ACDS(TEAM NM4) Notebook.ipynb

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   "source": [
    "# <font style='color: firebrick; text-align:center; display:block'> Climate Change Belief Analysis </font>\n",
    "---"
   ]
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    "#### Presented by:\n",
    "\n",
    "1. DANIEL IFEDIBA\n",
    "2. THERESAH KOOMSON\n",
    "3. TOCHUKWU EFEOKAFOR\n",
    "4. FRANK AYENSU\n",
    "5. PATRICK ONDUTO\n",
    "6. PUSELETSO LEGODI\n",
    "\n",
    "---"
   ]
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    "## <font id='TOC' color='goldenrod'>Table of Contents</font>\n",
    "\n",
    "### [ Table of Contents](#TOC)\n",
    "### 1. [Introduction](#Intro)\n",
    "\n",
    "#### 1.1. [Problem Description](#Intro1.1)\n",
    "#### 1.2. [Data Description](#Intro1.2)\n",
    "\n",
    "### 2. [Importing Packages](#Imports)\n",
    "\n",
    "### 3. [Loading Data](#Data)\n",
    "\n",
    "### 4. [Exploratory Data Analysis](#EDA)\n",
    "\n",
    "#### 4.1. [Identifying columns data type and missing values](#EDA4.1)\n",
    "#### 4.2. [Tweets message distribution](#EDA4.2)\n",
    "#### 4.3. [Tweets words and character analysis](#EDA4.3)\n",
    "##### 4.3.1  [Tweets word distribution](#EDA4.3.1)\n",
    "##### 4.3.2  [Tweet character distribution](#EDA4.3.2)\n",
    "##### 4.3.3  [Hashtags analysis](#EDA4.3.3)\n",
    "\n",
    "### 5. [Data Preprocessing](#Procs)\n",
    "\n",
    "#### 5.1. [Dealing with imbalanced data](#Procs5.1)\n",
    "#### 5.2. [Remove hyperlinks, twitter marks, and emojis](#Procs5.2)\n",
    "#### 5.3. [Creating text features](#Procs5.3')\n",
    "\n",
    "### 6. [Building the Models](#ModelB)\n",
    "\n",
    "### 7. [Tuning the Hyper Parameter of Models](#ModelT)\n",
    "\n",
    "### 8. [Evaluating our Model Performance](#ModelE)\n",
    "\n",
    "---"
   ]
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   "source": [
    "\n",
    "## <font id='Intro' color='goldenrod'>1. Introduction</font>"
   ]
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    "### <font id='Intro1.1'>1.1. Problem Description</font>\n",
    "Today, many companies are built around lessening one’s environmental impact or carbon footprint. They are offer products and services that are environmentally friendly and sustainable, in line with their values and ideals. A majority of these companies would like to determine how people perceive climate change and whether or not they believe it is a real threat. This would add to their market research efforts in gauging how their product/service may be received.\n",
    "\n",
    "With this context, we are creating a Machine Learning model that is able to classify whether or not a person believes in climate change, based on their novel tweet data.\n",
    "\n",
    "Providing an accurate and robust solution will give companies access to a broad base of consumer sentiment, spanning multiple demographic and geographic categories - thus increasing their insights and informing future marketing strategies."
   ]
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    "### <font id='Intro1.2'>1.2. Data Description</font>\n",
    "The data used to build the Machine models was funded by a Canada Foundation for Innovation JELF Grant to Chris Bauch, University of Waterloo.\n",
    "The dataset contains 43943 aggregates tweets pertaining to climate change collected between Apr 27, 2015 and Feb 21, 2018 with each tweet is labelled as one of the following classes:\n",
    "\n",
    "**Class Description**\n",
    " \n",
    "- 2 *``Moderate Belief``*: the tweet links to factual news about climate change\n",
    "- 1 *``High Belief``*: the tweet supports the belief of man-made climate change\n",
    "- 0 *``Neutral``*: the tweet neither supports nor refutes the belief of man-made climate change\n",
    "- -1 *``Low Belief``*: the tweet does not believe in man-made climate change\n",
    "\n",
    "**Variable definitions**\n",
    "- **sentiment**: Sentiment of tweet\n",
    "- **message**: Tweet body\n",
    "- **tweetid**: Twitter unique id\n",
    "\n",
    "For more on the data description [view source here](https://www.kaggle.com/competitions/edsa-climate-change-belief-analysis-2022/data)"
   ]
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   "source": [
    "## <font id='Imports' color='goldenrod'>2. Importing Packages</font>"
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   "source": [
    "Let's load python package that will be use to load the datasets, clean and format it, analysis and visualize it, and build, evaluate and draw the Machine Learning models"
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    "import warnings # to filter out warnings in the jupyter notebook\n",
    "warnings.filterwarnings('ignore') # we will ignore all warning and not show them\n",
    "\n",
    "import re # for creating regex expressions                               \n",
    "import string # for manipulating strings\n",
    "\n",
    "# Imports to load, explore, wrangle and visualize\n",
    "import numpy as np # for data manipulation\n",
    "import pandas as pd # for loading and data manipulation\n",
    "import matplotlib.pyplot as plt # for drawing visualizations\n",
    "import seaborn as sns # for drawing visualizations\n",
    "from wordcloud import WordCloud, STOPWORDS # for creating word cloud visualizations\n",
    "import nltk\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Imports for data preprocessing\n",
    "from nltk.corpus import stopwords # for generatig english stopwords\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer # for word stem/lemma production \n",
    "from nltk.tokenize import TweetTokenizer, TreebankWordTokenizer # for tokenizing text to words\n",
    "from sklearn.utils import resample # for resampling our dataset\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer # for vectorizing and transforming the data\n",
    "from sklearn.model_selection import train_test_split # for spliting data to training and testing sets\n",
    "\n",
    "# Imports for model building\n",
    "from sklearn.linear_model import LogisticRegression # for making a Logistic Regression classifier\n",
    "from sklearn.svm import SVC  # for making a Support Vector Machine classifier\n",
    "from sklearn.naive_bayes import MultinomialNB # for making a Naive Bayes classifier\n",
    "from sklearn.ensemble import RandomForestClassifier # for making a Random Forest classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier  # for making a K Neighbors classifier\n",
    "from sklearn.model_selection import GridSearchCV # for cross validation of models\n",
    "\n",
    "# Imports for model evaluation\n",
    "from sklearn.metrics import accuracy_score, classification_report, f1_score # for scoring our models\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix # for creating and drawing a confusion matrix respectively"
   ]
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   "source": [
    "## <font id='Data' color='goldenrod'>3. Loading Data</font>"
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    "Let's load the *training dataset*. This dataset will allow us to predict the sentiment of a person based on their tweet text.\n",
    "We will use pandas' (i.e. pd) dataframe to load and view as table."
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       "         sentiment                                            message\n",
       "tweetid                                                              \n",
       "625221           1  PolySciMajor EPA chief doesn't think carbon di...\n",
       "126103           1  It's not like we lack evidence of anthropogeni...\n",
       "698562           2  RT @RawStory: Researchers say we have three ye...\n",
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    "df = pd.read_csv(# load the dataset as dataframe, set tweetid as index column\n",
    "    './data/train.csv', index_col='tweetid', encoding='utf-8'\n",
    ") \n",
    "df.head() # view the first five row of the dataframe"
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    "After loading our data, let's view the shape of our dataframe to have a sense how the dataframe is constituted"
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    "# View the shape of the dataframe\n",
    "df.shape"
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    "Now that we have loaded and viewing the data as a dataframe, we can assertain from the first five rows of the dataframe that: \n",
    "- we have 3 columns in our dataset: ``tweetid``, ``sentiment`` and ``message`` column.\n",
    "- We make the ``tweetid`` column the index column of the dataframe as it has unique values.\n",
    "\n",
    "Also, the dataframe has *15819* rows (tweet observations) that we will be using to train and test our models with."
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    "## <font id='EDA' color='goldenrod'>4. Exploratory Data Analysis</font>"
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    "### <font id='EDA4.1'>4.1. Identifying columns data type and missing values</font>\n",
    "Let's first make sure that our data is correctly formated before analysing and using it. We will investigate the data type of each of the columns and also make sure that we have no missing values in our data and deal with any problems if present."
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-6fcfe4dd-0226-4ea3-953d-7681f25e8e7b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-6fcfe4dd-0226-4ea3-953d-7681f25e8e7b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           Total missing values\n",
       "sentiment                     0\n",
       "message                       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see if there are any missing values\n",
    "df.isna().sum().to_frame(name='Total missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25c08769",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "25c08769",
    "outputId": "e4c04ced-08fa-4cc1-9a02-cfc3dc524416",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15819 entries, 625221 to 806319\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   sentiment  15819 non-null  int64 \n",
      " 1   message    15819 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 370.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# view the data type of the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c946672",
   "metadata": {
    "id": "2c946672"
   },
   "source": [
    "After investigating the missing values and columns data types, we can conclude that our data is clean and thus does not need any further formating or imputation.\n",
    "The ``sentiment`` column of datatype ``int``, correct for Machine Learning models.\n",
    "Since this a Natural Language Processing Machine Learning problem, we will creating transforming our ``message`` column (of datatype ``object``) to create our features. (**NB:** This will be accomplished in the [Data preprocessing](##data_preprocessing) part.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd213ec",
   "metadata": {
    "id": "abd213ec"
   },
   "source": [
    "### <font id='EDA4.2'>4.2. Tweets message distribution</font>\n",
    "Next, we will investigate the amount of tweet messages allocated to each of the sentiment categories, giving a snapshot of our data distribution and highlighting any data inbalance in the dataset. This will ultimately inform if we need to employ to resampling technique to either upsample, downsample or both, to balance our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56292172",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "56292172",
    "outputId": "a600a8de-47de-461f-8fe5-880b439ef279",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Count Of Tweet Distribution Per Sentiment\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_60669_row0_col0 {\n",
       "  background-color: #aad466;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_60669_row0_col1, #T_60669_row1_col0 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_60669_row1_col1 {\n",
       "  background-color: #52a866;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_60669_row2_col0 {\n",
       "  background-color: #55aa66;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_60669_row2_col1 {\n",
       "  background-color: #259266;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_60669_row3_col0, #T_60669_row3_col1 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_60669_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >sentiment</th>\n",
       "      <th class=\"col_heading level0 col1\" >message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_60669_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "      <td id=\"T_60669_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_60669_row0_col1\" class=\"data row0 col1\" >8530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60669_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "      <td id=\"T_60669_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_60669_row1_col1\" class=\"data row1 col1\" >3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60669_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
       "      <td id=\"T_60669_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_60669_row2_col1\" class=\"data row2 col1\" >2353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60669_level0_row3\" class=\"row_heading level0 row3\" >0</th>\n",
       "      <td id=\"T_60669_row3_col0\" class=\"data row3 col0\" >-1</td>\n",
       "      <td id=\"T_60669_row3_col1\" class=\"data row3 col1\" >1296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f908d60c7d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create table to shows the sentiment types and the total number of tweets identified to each of them\n",
    "# Let's group dataframe by the sentiment column and aggregate our messages by counting them by sentiment category\n",
    "# sort by 'message counts' descending and then extract the 'message' column \n",
    "# a table that shows the sentiment types and the total number of tweets allocated to them\n",
    "print('Total Count Of Tweet Distribution Per Sentiment')\n",
    "\n",
    "class_df = df.groupby('sentiment').count()['message'].reset_index().sort_values(by='message',ascending=False) # grouping\n",
    "# the values of sentiments in descending order\n",
    "\n",
    "class_df.style.background_gradient(cmap='summer') # setting the display of the table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31205b3",
   "metadata": {
    "id": "d31205b3"
   },
   "source": [
    "Let's represent this information in a count plot to see how they are graphically distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7e40928",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "d7e40928",
    "outputId": "26fa1827-246e-426a-f98b-31957c35c1d9",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, let's visually view number of message per sentiment type\n",
    "ax = sns.countplot(\n",
    "    x=\"sentiment\",\n",
    "    data=df,\n",
    "    order=class_df.sentiment, # sorting the display of the sentiments so they appear in descending order\n",
    "    palette='BuGn_r') # draw a countplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19284ce4",
   "metadata": {
    "id": "19284ce4"
   },
   "source": [
    "Let's also plot a donut chart to know view percentage of tweets fall under each sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bdfd5fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "3bdfd5fb",
    "outputId": "c293b509-5e45-4f7c-d5e3-d5c7b1218dd0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A donut chart that shows the total percentage of tweet for each sentiment group\n",
    "percent_class=class_df.message # we group the size of the sentiments in percentage\n",
    "labels= class_df.sentiment\n",
    "\n",
    "colors = ['#1d763b','#3e7e54','#6f9f7f','#b3cfbd'] # we use hex codes to select the preferred colors of each section\n",
    "\n",
    "my_pie,_,_ = plt.pie(percent_class,radius = 2,labels=labels,colors=colors,autopct=\"%.1f%%\") # here we set the size of the chart\n",
    "\n",
    "plt.setp(my_pie, width=1, edgecolor='white') # set the color of the line that divides each section of the chart\n",
    "plt.title('Plot')\n",
    "plt.show() # displaying chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3897d3f0",
   "metadata": {
    "id": "3897d3f0"
   },
   "source": [
    "This is quite interesting! We can see that the distribution of the ``sentiment`` types are highly imbalanced with:\n",
    "- ``High Belief`` (1) sentiment has a total of 8530 messages, the largest portion\n",
    "-  ``Moderate Belief`` (2) sentiment has a total of 3640 messagess\n",
    "-  ``Neutral`` (0) sentiment has a total of 2353 messages\n",
    "- ``Low Belief`` (-1) sentiment has a total of 1296 messages, the smallest portion\n",
    "\n",
    "This implies that we need to resample to be able to achieve a balanced data set for training our models. This will make sure that the model predictions are not biased to *sentiments* with large portions of *messages*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c04f18",
   "metadata": {
    "id": "b0c04f18"
   },
   "source": [
    "### <font id='EDA4.3'>4.3. Tweets words and character analysis</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8c8e26",
   "metadata": {
    "id": "9b8c8e26"
   },
   "source": [
    "Seeing the percentage of tweets for each sentiment as shown above, we are then curious to understand more on the composition of our tweet messages in terms of words and characters distribution.\n",
    "We will first explore the words distribution, character distribution and later deal with twitter specific jargons i.e. hashtags and handles.\n",
    "\n",
    "Before we start exploring more about the words occuring in the tweets, we first need to create a dataframe that containing our words and the sentiments they occur in. to create the dataframe, we need to do a bit of cleaning to on the tweet messages like:\n",
    "- replacing the urls in the messages with single word, i.e. https, we still are interested in them too!\n",
    "- remove the RT in the beginning of the tweet messages indicating that one's re-posting someone else's content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1fac033",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "f1fac033",
    "outputId": "da3bbfe6-fa2b-48ce-9eb5-963a06b47411"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the words dataframe is (269225, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ea382889-ad5c-4082-918d-b873c336c06a\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>polyscimajor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>epa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>chief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>doesn't</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>think</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea382889-ad5c-4082-918d-b873c336c06a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
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       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ea382889-ad5c-4082-918d-b873c336c06a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ea382889-ad5c-4082-918d-b873c336c06a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   sentiment       message\n",
       "0          1  polyscimajor\n",
       "1          1           epa\n",
       "2          1         chief\n",
       "3          1       doesn't\n",
       "4          1         think"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's creates a visual to remove the clean and split our tweets message to words\n",
    "def make_tweet_words(tweet):\n",
    "    # remove the old style retweet text \"RT\"\n",
    "    tweet = re.sub(r'^RT[\\s]+', '', tweet)\n",
    "    # remove hyperlinks\n",
    "    tweet = re.sub(r'http[s]?://(?:[A-Za-z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9A-Fa-f][0-9A-Fa-f]))+', r'https', tweet.lower())\n",
    "    # return a list of words\n",
    "    return tweet.split(' ')\n",
    "\n",
    "words_df = df.copy() # make a copy of our dataframe\n",
    "words_df['message'] = words_df['message'].apply(make_tweet_words) # apply the make_tweet_words functionto create a list of words\n",
    "words_df = words_df.explode('message').reset_index(drop=True) # unstack the list to rows and reset the index. drop current\n",
    "print(f'The shape of the words dataframe is {words_df.shape}') # let's see the shape of our dataframe\n",
    "words_df.head() # let's view the head of our dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f7422e",
   "metadata": {
    "id": "c4f7422e"
   },
   "source": [
    "We can observe that the dataframe containing our words has *2* columns and *269, 225* rows representing words."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741be13a",
   "metadata": {
    "id": "741be13a"
   },
   "source": [
    "#### <font id='EDA4.3.1'>4.3.1. Tweets word distribution</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a59194",
   "metadata": {
    "id": "d9a59194"
   },
   "source": [
    "Exploring the words frequency will help us under stand the over all topics and what is going on on the twitter conversation. Its distribution will guide on how we handle feature creation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adaae546",
   "metadata": {
    "id": "adaae546"
   },
   "source": [
    "Now, that we have our words, lets explore them further. We will create a word cloud visual to give us an idea what are the top occuring 200 words (default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42f6b510",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "42f6b510",
    "outputId": "d4932fc7-336a-4810-fc6e-763cf6e38f15"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcount_stopwords = set(STOPWORDS) # set word cloud stopwords \n",
    "words = ' '.join(words_df.message) # join the words we created for to a single string\n",
    "wordcloud = WordCloud(width = 1600, height = 800, # instantiate the word cloud with 1600x800 area\n",
    "                stopwords = wordcount_stopwords, #  the stopword to use\n",
    "                min_font_size = 10 # and a font size of 10\n",
    "                     )\n",
    "wordcloud.generate(words) # generate the word cloud image\n",
    "\n",
    "# Let's plot the WordCloud image                      \n",
    "plt.figure(figsize = (16, 8), facecolor = None) # create a plot area size 16x8\n",
    "plt.imshow(wordcloud) # plot the word cloud image\n",
    "plt.axis(\"off\") # remove the axis\n",
    " \n",
    "plt.show() # show the word cloud plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c0f5ef",
   "metadata": {
    "id": "a5c0f5ef"
   },
   "source": [
    "Climate change, global warming, web urls, believe, trump and amp... what a concontion. Seems topics about chimate change elicite different reactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0451e3e",
   "metadata": {
    "id": "b0451e3e"
   },
   "source": [
    "Now to understand more about our messages, a distribution of the total number off words in each of our tweets. We will plot a histogram for this distribution for each of the sentiment categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3e95ec85",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "3e95ec85",
    "outputId": "c859cda9-926b-4226-c197-8c0819b66169",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we plot the total number of words in a tweet per sentiment\n",
    "fig,((ax1,ax2),(ax3,ax4))=plt.subplots(2,2,figsize=(10,10))\n",
    "\n",
    "# using the split command and lambda function\n",
    "# to sperate each word in the each tweet for high belief\n",
    "tweet_len=df[df['sentiment']==2]['message'].str.split().map(lambda x: len(x))\n",
    "ax1.hist(tweet_len,color='#1d763b') # using hex codes to select color for graph 1\n",
    "ax1.set_title('High Belief') # setting the title for grapgh 1\n",
    "\n",
    "# using the split command and lambda function\n",
    "# to sperate each word in the each tweet for moderate belief\n",
    "tweet_len=df[df['sentiment']==1]['message'].str.split().map(lambda x: len(x))\n",
    "ax2.hist(tweet_len,color='#3e7e54') # using hex codes to select color for graph 2\n",
    "ax2.set_title('Moderate Belief') # setting the title for grapgh 2\n",
    "\n",
    "# using the split command and lambda function\n",
    "# to sperate each word in the each tweet for neutral\n",
    "tweet_len=df[df['sentiment']==0]['message'].str.split().map(lambda x: len(x))\n",
    "ax3.hist(tweet_len,color='#6f9f7f') # using hex codes to select color for graph 3\n",
    "ax3.set_title('Neutral') # setting the title for grapgh 3\n",
    "\n",
    "# using the split command and lambda function\n",
    "# to sperate each word in the each tweet for low belief\n",
    "tweet_len=df[df['sentiment']==-1]['message'].str.split().map(lambda x: len(x))\n",
    "ax4.hist(tweet_len,color='#b3cfbd') # using hex codes to select color for graph 4\n",
    "ax4.set_title('Low Belief') # setting the title for grapgh 3\n",
    "\n",
    "fig.suptitle('Words in a tweet') # setting the overall plot title\n",
    "plt.show() # we set the size of our graphs and make them 2x2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17835634",
   "metadata": {
    "id": "17835634"
   },
   "source": [
    "Hmmmm, this looks interesting, now i wonder how many average words there could be for each tweet per sentiment, let's check that out shall we?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e769b691",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 695
    },
    "id": "e769b691",
    "outputId": "ae656cb6-499f-4a2c-b552-05cf75fda1a8",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Average word length in each tweet')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we print the average tweet lenght per sentiment\n",
    "fig,((ax1,ax2), (ax3,ax4))=plt.subplots(2,2,figsize=(10,10))\n",
    "\n",
    "word=df[df['sentiment']==2]['message'].str.split().apply(lambda x : [len(i) for i in x]) # using the str.split to seprate each\n",
    "# word in a tweet then use the lambda function to get the length\n",
    "\n",
    "sns.histplot(word.map(lambda x: np.mean(x)),ax=ax1,color='#1d763b') # using the np.mean to get the average of words lenght,\n",
    "# and also using hex codes to set color\n",
    "\n",
    "ax1.set_title('High Belief') # setting the title for grapgh 1\n",
    "\n",
    "word=df[df['sentiment']==1]['message'].str.split().apply(lambda x : [len(i) for i in x])  # using the str.split to seprate each\n",
    "# word in a tweet then use the lambda function to get the length\n",
    "\n",
    "sns.histplot(word.map(lambda x: np.mean(x)),ax=ax2,color='#3e7e54')  # using the np.mean to get the average of words lenght,\n",
    "# and also using hex codes to set color\n",
    "\n",
    "ax2.set_title('Moderate Belief') # setting the title for grapgh 2\n",
    "\n",
    "word=df[df['sentiment']==0]['message'].str.split().apply(lambda x : [len(i) for i in x])  # using the str.split to seprate each\n",
    "# word in a tweet then use the lambda function to get the length\n",
    "\n",
    "sns.histplot(word.map(lambda x: np.mean(x)),ax=ax3,color='#6f9f7f')  # using the np.mean to get the average of words lenght,\n",
    "# and also using hex codes to set color\n",
    "\n",
    "ax3.set_title('Neutral Belief') # setting the title for grapgh 3\n",
    "\n",
    "word=df[df['sentiment']==-1]['message'].str.split().apply(lambda x : [len(i) for i in x])  # using the str.split to seprate each\n",
    "# word in a tweet then use the lambda function to get the length\n",
    "\n",
    "sns.histplot(word.map(lambda x: np.mean(x)),ax=ax4,color='#b3cfbd')  # using the np.mean to get the average of words lenght,\n",
    "# and also using hex codes to set color\n",
    "\n",
    "ax4.set_title('Low Belief') # setting the title for grapgh 4\n",
    "\n",
    "fig.suptitle('Average word length in each tweet') # setting the overall plot title"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec9eea5",
   "metadata": {
    "id": "9ec9eea5"
   },
   "source": [
    "#### <font id='4.3.2'>4.3.2. Tweet character distribution</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8714282",
   "metadata": {
    "id": "c8714282"
   },
   "source": [
    "What about the character used in the tweet messages, let's take a look their distributions for each sentiment class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14f45f5a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "14f45f5a",
    "outputId": "6192bc76-a544-4be4-b56b-b926f3d7480d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,((ax1,ax2),(ax3,ax4))=plt.subplots(2,2,figsize=(10,10)) # we set the size of our graphs and make them 2x2\n",
    "\n",
    "# here we use the .str.len to find the total characters for each tweet\n",
    "# in high belief\n",
    "tweet_len=df[df['sentiment']==2]['message'].str.len() \n",
    "ax1.hist(tweet_len,color='#1d763b') # using hex codes to select color for graph 1\n",
    "ax1.set_title('High Belief') # for sentiment 2, we name it High Belief\n",
    "\n",
    "# here we use the .str.len to find the total characters for each tweet\n",
    "# in moderate belief\n",
    "tweet_len=df[df['sentiment']==1]['message'].str.len() \n",
    "ax2.hist(tweet_len,color='#3e7e54') # using hex codes to select color for graph 2\n",
    "ax2.set_title('Moderate Belief') # for sentiment 1, we name it Moderate Belief\n",
    "\n",
    "# here we use the .str.len to find the total characters for each tweet\n",
    "# in neutral\n",
    "tweet_len=df[df['sentiment']==0]['message'].str.len()\n",
    "ax3.hist(tweet_len,color='#6f9f7f') # using hex codes to select color for graph 3\n",
    "ax3.set_title('Neutral') # for sentiment 0, we name it neutral\n",
    "\n",
    "# here we use the .str.len to find the total characters for each tweet\n",
    "# in low belief\n",
    "tweet_len=df[df['sentiment']==-1]['message'].str.len()\n",
    "ax4.hist(tweet_len,color='#b3cfbd') # using hex codes to select color for graph 4\n",
    "ax4.set_title('Low Belief') # for sentiment -1, we name it Low Belief\n",
    "\n",
    "fig.suptitle('Characters in tweets') # setting the overall plot title\n",
    "plt.show() # display the graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d4e3ac",
   "metadata": {
    "id": "52d4e3ac"
   },
   "source": [
    "Wow, that's a lot of characters used, seems people can't get their hands off their phones, so why don't we chaeck the amount of words used in tweets for each sentiment.\n",
    "We'll represent this graphically using a histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0eea4e",
   "metadata": {
    "id": "1b0eea4e"
   },
   "source": [
    "Seeing the average word length above, we now have an understanding of our dataset and the meesages(tweets) by users and how they unfold.\n",
    "\n",
    "The next thing we can do is to look at the hashtags for each sentiment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6879a3e",
   "metadata": {
    "id": "b6879a3e"
   },
   "source": [
    "#### <font id='EDA4.3.3'>4.3.3. Hashtags analysis</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a91d2bb",
   "metadata": {
    "id": "6a91d2bb"
   },
   "source": [
    "Since we are dealing with tweets, we might get interesting insights regarding hashtags. But to make sense of them, we first need to extract them from the tweets.\n",
    "We will create a function to extract the hashtags, then get the length of each hashtag for each sentiment and then use it to make a table which will be represented in a seasborn countplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1873e2ac",
   "metadata": {
    "id": "1873e2ac"
   },
   "outputs": [],
   "source": [
    "# creating a function that extracts hashtags from tweets\n",
    "def hashtag_extract(tweets):\n",
    "    hashtags = [] # a list to put in the words that have hashtags beside them\n",
    "    # loop words in the tweet\n",
    "    for tweet in tweets:\n",
    "        ht = re.findall(r'#(\\w+)', tweet)\n",
    "        hashtags.append(ht)\n",
    "    return hashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8160bd4",
   "metadata": {
    "id": "d8160bd4"
   },
   "outputs": [],
   "source": [
    "# extract hashtags from tweets\n",
    "# applying the function to each tweet sentiment, and assigning them to a variable\n",
    "ht_high_belief = hashtag_extract(df['message'][df['sentiment']==2])\n",
    "\n",
    "ht_moderate_belief = hashtag_extract(df['message'][df['sentiment']==1])\n",
    "\n",
    "ht_neutral = hashtag_extract(df['message'][df['sentiment']==0])\n",
    "\n",
    "ht_low_belief = hashtag_extract(df['message'][df['sentiment']==-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b1189a8",
   "metadata": {
    "id": "0b1189a8"
   },
   "outputs": [],
   "source": [
    "# geting the sum total of each word that has an hashtag beside them\n",
    "ht_high_belief = sum(ht_high_belief, [])\n",
    "ht_moderate_belief = sum(ht_moderate_belief, [])\n",
    "ht_neutral = sum(ht_neutral, [])\n",
    "ht_low_belief = sum(ht_low_belief, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea2a62bb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ea2a62bb",
    "outputId": "7c75b749-1397-405c-c12d-8cd2e2ab1fdd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_09b94_row0_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09b94_row1_col1 {\n",
       "  background-color: #158a66;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09b94_row2_col1 {\n",
       "  background-color: #098466;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09b94_row3_col1 {\n",
       "  background-color: #198c66;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09b94_row4_col1 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_09b94_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Hashtag</th>\n",
       "      <th class=\"col_heading level0 col1\" >Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_09b94_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_09b94_row0_col0\" class=\"data row0 col0\" >climate</td>\n",
       "      <td id=\"T_09b94_row0_col1\" class=\"data row0 col1\" >130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09b94_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_09b94_row1_col0\" class=\"data row1 col0\" >ClimateChange</td>\n",
       "      <td id=\"T_09b94_row1_col1\" class=\"data row1 col1\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09b94_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_09b94_row2_col0\" class=\"data row2 col0\" >CLIMATEchange</td>\n",
       "      <td id=\"T_09b94_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09b94_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_09b94_row3_col0\" class=\"data row3 col0\" >p2</td>\n",
       "      <td id=\"T_09b94_row3_col1\" class=\"data row3 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09b94_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_09b94_row4_col0\" class=\"data row4 col0\" >skpol</td>\n",
       "      <td id=\"T_09b94_row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f90890b8750>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using freqdist to show a table  with the frequency of words for hastags that fall under high belief sentiment\n",
    "freq = nltk.FreqDist(ht_high_belief)\n",
    "d1 = pd.DataFrame({'Hashtag': list(freq.keys()),\n",
    "                  'Count': list(freq.values())})\n",
    "d1.head().style.background_gradient(cmap='summer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd26245d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bd26245d",
    "outputId": "f71d223d-d1a5-4edb-c251-2e3df84882ae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  background-color: #168a66;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_44c8f_row4_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_44c8f_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Hashtag</th>\n",
       "      <th class=\"col_heading level0 col1\" >Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_44c8f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_44c8f_row0_col0\" class=\"data row0 col0\" >TodayinMaker</td>\n",
       "      <td id=\"T_44c8f_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44c8f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_44c8f_row1_col0\" class=\"data row1 col0\" >ElectionNight</td>\n",
       "      <td id=\"T_44c8f_row1_col1\" class=\"data row1 col1\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44c8f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_44c8f_row2_col0\" class=\"data row2 col0\" >climatemarch</td>\n",
       "      <td id=\"T_44c8f_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44c8f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_44c8f_row3_col0\" class=\"data row3 col0\" >SaveOurPlanet</td>\n",
       "      <td id=\"T_44c8f_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44c8f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_44c8f_row4_col0\" class=\"data row4 col0\" >COP22</td>\n",
       "      <td id=\"T_44c8f_row4_col1\" class=\"data row4 col1\" >59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f90890b8810>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using freqdist to show a table  with the frequency of words for hastags that fall under moderate belief sentiment\n",
    "freq = nltk.FreqDist(ht_moderate_belief)\n",
    "d2 = pd.DataFrame({'Hashtag': list(freq.keys()),\n",
    "                  'Count': list(freq.values())})\n",
    "d2.head().style.background_gradient(cmap='summer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3efb619",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "c3efb619",
    "outputId": "bd91a55d-e66f-4c82-e34a-681e6cfb5c33"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e8777_row0_col1, #T_e8777_row1_col1, #T_e8777_row2_col1, #T_e8777_row3_col1 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e8777_row4_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e8777_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Hashtag</th>\n",
       "      <th class=\"col_heading level0 col1\" >Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e8777_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e8777_row0_col0\" class=\"data row0 col0\" >qanda</td>\n",
       "      <td id=\"T_e8777_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8777_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e8777_row1_col0\" class=\"data row1 col0\" >h</td>\n",
       "      <td id=\"T_e8777_row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8777_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_e8777_row2_col0\" class=\"data row2 col0\" >COAG</td>\n",
       "      <td id=\"T_e8777_row2_col1\" class=\"data row2 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8777_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_e8777_row3_col0\" class=\"data row3 col0\" >airquality</td>\n",
       "      <td id=\"T_e8777_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8777_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_e8777_row4_col0\" class=\"data row4 col0\" >ClimateChange</td>\n",
       "      <td id=\"T_e8777_row4_col1\" class=\"data row4 col1\" >4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f90893f5790>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using freqdist to show a table  with the frequency of words for hastags that fall under nuetral sentiment\n",
    "freq = nltk.FreqDist(ht_neutral)\n",
    "d3 = pd.DataFrame({'Hashtag': list(freq.keys()),\n",
    "                  'Count': list(freq.values())})\n",
    "d3.head().style.background_gradient(cmap='summer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e18ab0b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "6e18ab0b",
    "outputId": "11126e20-ab32-4bce-f6bc-4040a8aaa746"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_66858_row0_col1, #T_66858_row1_col1, #T_66858_row4_col1 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_66858_row2_col1 {\n",
       "  background-color: #80c066;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_66858_row3_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_66858_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Hashtag</th>\n",
       "      <th class=\"col_heading level0 col1\" >Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_66858_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_66858_row0_col0\" class=\"data row0 col0\" >NoMoreSCAMS</td>\n",
       "      <td id=\"T_66858_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66858_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_66858_row1_col0\" class=\"data row1 col0\" >NoMoreDEMOCRATS</td>\n",
       "      <td id=\"T_66858_row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66858_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_66858_row2_col0\" class=\"data row2 col0\" >DrainTheSwamp</td>\n",
       "      <td id=\"T_66858_row2_col1\" class=\"data row2 col1\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66858_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_66858_row3_col0\" class=\"data row3 col0\" >fakenews</td>\n",
       "      <td id=\"T_66858_row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66858_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_66858_row4_col0\" class=\"data row4 col0\" >myth</td>\n",
       "      <td id=\"T_66858_row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9085836090>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using freqdist to show a table  with the frequency of words for hastags that fall under low belief sentiment\n",
    "freq = nltk.FreqDist(ht_low_belief)\n",
    "d4 = pd.DataFrame({'Hashtag': list(freq.keys()),\n",
    "                  'Count': list(freq.values())})\n",
    "d4.head().style.background_gradient(cmap='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78357aa",
   "metadata": {
    "id": "f78357aa"
   },
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf33e42",
   "metadata": {
    "id": "8bf33e42"
   },
   "source": [
    "Representing the values of each table on a count plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e68f2761",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "e68f2761",
    "outputId": "2c9032cc-3532-4e07-c820-f85c7e5c62d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Count Of Hashtags For High Belief')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1 = d1.nlargest(columns='Count', n=10)\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(data=d1, x='Hashtag', y='Count',palette='BuGn_r').set(title='Count Of Hashtags For High Belief')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f1b38d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "5f1b38d3",
    "outputId": "223177aa-89db-47c5-d314-4bc0223755a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Count Of Hashtags For Moderate Belief')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2 = d2.nlargest(columns='Count', n=10)\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(data=d2, x='Hashtag', y='Count',palette='BuGn_r').set(title='Count Of Hashtags For Moderate Belief')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e102756",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "7e102756",
    "outputId": "e8be3f35-8e7b-4d0a-857a-fa876d459a0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Count Of Hashtags For Neutral')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d3 = d3.nlargest(columns='Count', n=10)\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(data=d3, x='Hashtag', y='Count',palette='BuGn_r').set(title='Count Of Hashtags For Neutral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6025c975",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "6025c975",
    "outputId": "e9400a6b-cb9a-4d59-f5d5-52b6b8eaf707"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Count Of Hashtags For Low Belief')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d4 = d4.nlargest(columns='Count', n=10)\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(data=d4, x='Hashtag', y='Count',palette='BuGn_r').set(title='Count Of Hashtags For Low Belief')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60aecab6",
   "metadata": {
    "id": "60aecab6"
   },
   "source": [
    "## <font id='Procs' color='goldenrod'>5. Data Preprocessing</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a487e3",
   "metadata": {
    "id": "c2a487e3"
   },
   "source": [
    "Data preprocessing is one of the critical steps in any machine learning project. It includes cleaning and formatting the data before feeding into a machine learning algorithm.\n",
    "Also, to remove learning bias from our models, we need to balance our data when we have an inbalanced classes in our dataset.\n",
    "For NLP, the preprocessing steps are comprised of the following tasks:\n",
    "- Tokenizing the string\n",
    "- Lowercasing\n",
    "- Removing stop words and punctuation\n",
    "- Stemming"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de0ebd3",
   "metadata": {
    "id": "3de0ebd3"
   },
   "source": [
    "### <font id='Procs5.1'>5.1. Dealing with Imbalanced data</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55ad86e",
   "metadata": {
    "id": "f55ad86e"
   },
   "source": [
    "Since we have imbalanced data for our classes and we need the models to predict all the classes without any issue, we need to consider balancing it. While a slight imbalance wouldn’t be a problem, a highly imbalanced dataset like ours can cause issues to the classification predictions. And as most machine learning algorithms rely on sufficient data, the algorithm can’t correctly predict its result when some of the classes have little data.\n",
    "\n",
    "Now, let's balance our data. With the ``Pro`` (1) has the largest proportion of **8530** we use that to set our sample size for each of the sentiment classes, say roughly about half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9O956amjy7SK",
   "metadata": {
    "id": "9O956amjy7SK"
   },
   "outputs": [],
   "source": [
    "# Let's pick a class size of roughly half the size of the largest size\n",
    "class_size = 5000\n",
    "# Let's list the target labels\n",
    "labels_counts = df['sentiment'].value_counts().to_dict() # create a dict with the setiment  as keys and counts as values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b8862f",
   "metadata": {
    "id": "07b8862f"
   },
   "source": [
    "We, need to have now downsample the ``Pro`` (1) sentiment class while we upsample all the other sentiment classes by setting the ``replace`` attribute of skit learn's ``resample`` method to ``False`` and ``True`` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "LpJXtTSdy-yT",
   "metadata": {
    "id": "LpJXtTSdy-yT"
   },
   "outputs": [],
   "source": [
    "resampled_classes = []\n",
    "\n",
    "# For each label\n",
    "for label, label_size in labels_counts.items():\n",
    "    # If label_size < class size then set replace to True to upsample, else False to downsample\n",
    "    if label_size < class_size:\n",
    "        # Upsample\n",
    "        replacement = True # sample with replacement (we need to duplicate observations)\n",
    "    else:\n",
    "        # Downsample\n",
    "        replacement = False # sample with replacement (we need to duplicate observations)\n",
    "    label_data = df[df['sentiment'] == label]\n",
    "    label_resampled = resample(label_data,\n",
    "                               replace=replacement, # sample without replacement (no need for duplicate observations)\n",
    "                               n_samples=class_size, # number of desired samples\n",
    "                               random_state=27) # reproducible results\n",
    "\n",
    "    resampled_classes.append(label_resampled) # add to the resampled_classes list the sampled dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff74b56",
   "metadata": {
    "id": "7ff74b56"
   },
   "source": [
    "Finally, let's concatenate all the resample dataframes to have a final balanced dataset to train our models on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d70qXELn2tnD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d70qXELn2tnD",
    "outputId": "45c5e61b-6918-44e7-da40-2af1aaabec38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 2)\n"
     ]
    }
   ],
   "source": [
    "resampled_data = pd.concat(resampled_classes, axis=0) # add the dataframes for the list together to have a final dataframe\n",
    "print(resampled_data.shape) # view the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2b577fb2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "2b577fb2",
    "outputId": "f356bdce-b21d-41a8-84ec-62859363da2f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MuCdJWjIWxWWoqroryauATwLLgM1Vdf2E25oLi+qy2SLg5zm3/DznzqL/LBfFDW5J0mQtlstQkqQJMiwkSV2GxQQk2ZxkZ5IvTbqXxS7J4UkuS3JDkuuTvHrSPS12PlpnbiV5SpLPJbkzyesm3c8D5T2LCUjyC8D3gPOr6mmT7mcxS3IYcFhVfT7Jo4CrgVOq6oYJt7YotUfrfAV4HrCd0UjEl/h5PnBJHgc8CTgF2FNVfzbhlh4QzywmoKo+A+yedB8Hgqq6pao+36a/C9wIrJpsV4uaj9aZY1W1s6quAv530r08GIaFDhhJ1gDPBK6YbCeL2irg5rH57Ri+wrDQASLJI4GPAK+pqu9Muh/pQGNYaNFLchCjoPjbqvrHSfezyPlonTmQ5Mwk17TXEybdz1wwLLSoJQlwHnBjVb1z0v0cAHy0zhyoqr+oqiPb65uT7mcuOBpqApJ8GDiO0WOLbwXOqqrzJtrUIpXk54DPAtcBP2zlN1bVJybX1eKW5CTgXfz/o3XOnnBLi1qSxwPbgEcz+m/0e8C6xXa51LCQJHV5GUqS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhTTHkhzZhp/unX/B0E9vTXJckp8d8hha2gwLae4dCfwoLKrqkqp628DHPA4wLDQYv2chjUnyCOAiRo+5WAa8BZgG3gk8ErgNeEVV3ZLk04weWvgc4BDgjDY/DTyc0WMy/qRNr6+qVyX5IPDfjB54+DjglcDLgWcBV1TVK1ofJwB/DDwU+CpwelV9L8nXgS3ArwAHAS8CfgBcDtwN7AJ+p6o+O8Tno6XLMwvpnjYA36yqZ7TfGvln4L3AqVX1M8BmYPwbzcur6mjgNYy+if8/wB8BF7ZHPVy4j2OsYBQOv8voURrnAE8Fnt4uYa0E/hD4xao6itG3f39vbPvbWv39wOuq6uvAB4Bz2jENCs255ZNuQFpgrgPekeTtwMeBPcDTgK2jx1CxDLhlbP29Dy68Glgzy2N8rKoqyXXArVV1HUCS69s+VgPrgH9vxzwY+NwMx3zhfvxt0gNmWEhjquorSY5idM/hrcCngOur6lkzbHJne7+b2f972rvND8em984vb/vaWlUvmcNjSg+Kl6GkMe1x0t+vqr8B/hQ4BphK8qy2/KAkT+3s5rvAox5EG5cDz07y5HbMRyT5yYGPKd0vw0K6p6cDVya5BjiL0f2HU4G3J/kicA39UUeXAevabxn82v42UFW7gFcAH05yLaNLUE/pbPYx4FfbMX9+f48p9TgaSpLU5ZmFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnq+j/gfPSBYIkKJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a bar graph to now show the final distribution classes of the resampled dataframe\n",
    "ax = sns.countplot(x=\"sentiment\", data=resampled_data, palette='BuGn_r', order=[1, 2, 0, -1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f19b312",
   "metadata": {
    "id": "2f19b312"
   },
   "source": [
    "Our datase now contains 20,000 tweets in total distributed 5,000 tweets four way making our classses have a balanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e8fd67",
   "metadata": {
    "id": "27e8fd67"
   },
   "source": [
    "### <font id='Procs5.2'>5.2. Remove hyperlinks, twitter marks, and emojis</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28964d2d",
   "metadata": {
    "id": "28964d2d"
   },
   "source": [
    "Let's explore some of tweets had see how they are composed to understand on how to now process the messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cbac2b61",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cbac2b61",
    "outputId": "a1e1e37c-0253-471d-de6f-6e7cdd1caa02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PolySciMajor EPA chief doesn't think carbon dioxide is main cause of global warming and.. wait, what!? https://t.co/yeLvcEFXkC via @mashable\n",
      "\n",
      "RT @RawStory: Researchers say we have three years to act on climate change before it’s too late https://t.co/WdT0KdUr2f https://t.co/Z0ANPT…\n",
      "\n",
      "#TodayinMaker# WIRED : 2016 was a pivotal year in the war on climate change https://t.co/44wOTxTLcD\n",
      "\n",
      "@MissLizzyNJ lmao 😂 snowflakes ❄️ complaining about snowflakes ❄️ in winter =global warming 🤦‍♂️\n",
      "\n",
      "RT @NDeNicolaMD: 500,000 doctors agree: climate change is making us sick #EarthDay #MarchForScience #HealthAndClimate…\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tweetids_to_explore = df.loc[[625221, 698562, 573736, 911385, 483815], 'message'] # select a sample of the tweets\n",
    "for m in tweetids_to_explore:\n",
    "    print(m, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41207654",
   "metadata": {
    "id": "41207654"
   },
   "source": [
    "From the five tweets we sampled, we can observe the following about our tweet messages.\n",
    "\n",
    "- the beginning tweet **RT** that indicates one's re-posting someone else's content\n",
    "- the official **@** and **#** character signifying handles and trends respectively\n",
    "- the punctuations marks, emoticons, urls etc.\n",
    "\n",
    "We need to do a lot of preprocessing to work have good clean data to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3d7217",
   "metadata": {
    "id": "ce3d7217"
   },
   "source": [
    "First, let's creat a function to strip the emoticons from the tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2749139",
   "metadata": {
    "id": "f2749139"
   },
   "outputs": [],
   "source": [
    "# function to strip emojis from the tweets\n",
    "#Ref: https://www.kaggle.com/code/eliasdabbas/how-to-create-a-python-regex-to-extract-emoji/notebook\n",
    "# Ref: https://gist.github.com/Alex-Just/e86110836f3f93fe7932290526529cd1\n",
    "# Ref: https://gist.github.com/Alex-Just/e86110836f3f93fe7932290526529cd1#gistcomment-3208085\n",
    "# Ref: https://en.wikipedia.org/wiki/Unicode_block\n",
    "\n",
    "def remove_emoji(text):\n",
    "    EMOJI_PATTERN = re.compile(\n",
    "        \"([\"\n",
    "        \"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "        \"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "        \"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "        \"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "        \"\\U0001F700-\\U0001F77F\"  # alchemical symbols\n",
    "        \"\\U0001F780-\\U0001F7FF\"  # Geometric Shapes Extended\n",
    "        \"\\U0001F800-\\U0001F8FF\"  # Supplemental Arrows-C\n",
    "        \"\\U0001F900-\\U0001F9FF\"  # Supplemental Symbols and Pictographs\n",
    "        \"\\U0001FA00-\\U0001FA6F\"  # Chess Symbols\n",
    "        \"\\U0001FA70-\\U0001FAFF\"  # Symbols and Pictographs Extended-A\n",
    "        \"\\U00002702-\\U000027B0\"  # Dingbats\n",
    "        \"\\U000024C2-\\U0001F251\"\n",
    "        \"])\"\n",
    "    )\n",
    "    text = re.sub(EMOJI_PATTERN,  '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79aba96",
   "metadata": {
    "id": "c79aba96"
   },
   "source": [
    "Now, we need to create a function that actually handles the processing a tweet message, removing all emoticons, punctuation marks, tweet handles and trends, and removes english stopword.\n",
    "We then create lemmas of the remaining words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6f7ef0d",
   "metadata": {
    "id": "d6f7ef0d"
   },
   "outputs": [],
   "source": [
    "def tweet_preprocessor(tweet):\n",
    "    # remove the old style retweet text \"RT\"\n",
    "    tweet_clean = re.sub(r'^RT[\\s]+', '', tweet)\n",
    "\n",
    "    # remove hashtags. We have to be careful here not to remove \n",
    "    # the whole hashtag because text of hashtags contains huge information. \n",
    "    # only remove the hash # sign from the word\n",
    "    tweet_clean = re.sub(r'#', '', tweet_clean)\n",
    "\n",
    "    # remove hyperlinks\n",
    "    tweet_clean = re.sub(\n",
    "        r'http[s]?://(?:[A-Za-z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9A-Fa-f][0-9A-Fa-f]))+'\n",
    "        ,r'url-web',\n",
    "        tweet_clean)\n",
    "\n",
    "    # remove single numeric terms in the tweet. \n",
    "    tweet_clean = re.sub(r'\\s[0-9]+\\s', '', tweet_clean)\n",
    "\n",
    "    # remove emojis from in the tweet\n",
    "    tweet_clean = remove_emoji(tweet_clean)\n",
    "\n",
    "    # remove punctuation from the tweet\n",
    "    tweet_clean = ''.join([l for l in tweet_clean if l not in string.punctuation])\n",
    "\n",
    "    # convert tweet to lowercase and return it\n",
    "    tweet_clean = tweet_clean.lower()\n",
    "    \n",
    "    # tokenize the tweet\n",
    "    tokenizer = TweetTokenizer() #Instantiate the tokenizer class\n",
    "    tweet_tokens = tokenizer.tokenize(tweet_clean)\n",
    "    \n",
    "    # remove stop words\n",
    "    stopwords_english = stopwords.words('english')\n",
    "    tweet_tokens_without_stopwords = [t for t in tweet_tokens if t not in stopwords_english]\n",
    "    \n",
    "    # stem the tweet\n",
    "    #stemmer = PorterStemmer()\n",
    "    #tweet_stems = ' '.join([stemmer.stem(t) for t in tweet_tokens])\n",
    "    \n",
    "    # stem the tweet\n",
    "    lemma = WordNetLemmatizer()\n",
    "    tweet_stems = ' '.join([lemma.lemmatize(t) for t in tweet_tokens])\n",
    "    \n",
    "    return tweet_stems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aaad589",
   "metadata": {
    "id": "3aaad589"
   },
   "source": [
    "### <font id='Procs5.3'>5.3. Creating text features</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2a4c1f",
   "metadata": {
    "id": "9f2a4c1f"
   },
   "source": [
    " After creating our functions, we will need to to split the dataset into features and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ede02688",
   "metadata": {
    "id": "ede02688"
   },
   "outputs": [],
   "source": [
    "#Get the features and targets\n",
    "X = resampled_data['message']\n",
    "y = resampled_data['sentiment']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb6bce88",
   "metadata": {
    "id": "eb6bce88"
   },
   "source": [
    "The text documents in their original form can not be processed by the model classifiers and learning algorithms as most of them expect numerical feature vectors.\n",
    "\n",
    "Rather than the raw text documents that have variable length, we will convert the ``message`` column to a bag of words vector using the skit learn's ``CountVectorizer`` to produce our features.\n",
    "We will, specifically, calculate a measure called Term Frequency, Inverse Document Frequency, abbreviated to tf-idf, for each term in the vectorized features using skit learn's ``TfidfTransformer``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa48d642",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fa48d642",
    "outputId": "2bd84800-323b-4cb8-a58a-ba0cb5c9ba67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 50602)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer( # Instantiate the object\n",
    "    preprocessor=tweet_preprocessor,\n",
    "    min_df=2, # with min_df of 2 words in our bag of words\n",
    "    max_df=0.5, # with max_df of 50% word frequency in our bag of words\n",
    "    ngram_range=(1,2) # with ngram_range of (1,2) for bag of words\n",
    ") \n",
    "vectorizer.fit(X) #build vocabulary for training\n",
    "X_tokenized = vectorizer.transform(X) #encode the text data \n",
    "\n",
    "# scale the encoded text data \n",
    "tfidf_transformer = TfidfTransformer() # Instantiate the object\n",
    "X_trans = tfidf_transformer.fit_transform(X_tokenized) # Transform the tokens\n",
    "X_trans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf8b268",
   "metadata": {
    "id": "abf8b268"
   },
   "source": [
    "Now, each of the *20000* tweet messages is represented by *50602* features, representing the tf-idf score for different unigrams and bigrams."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40456f17",
   "metadata": {
    "id": "40456f17"
   },
   "source": [
    "## <font id='ModelB' color='goldenrod'>6. Building the Models </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec56d7a5",
   "metadata": {
    "id": "ec56d7a5"
   },
   "source": [
    "Now it is time to build our models.\n",
    "\n",
    "For our of problem we are going to experiment with five different algorithms and explore their performance to find the performing best model.\n",
    "\n",
    "Before we build the models, we are going to first split our features and labels into training and testing sets, create a list object containing our benchmark models and the hyper parameters that we will use to tune them (later), and lastly, we create functions to fit and evaluate the models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91abb8a1",
   "metadata": {
    "id": "91abb8a1"
   },
   "source": [
    "Let's then split the features and labels to training and testing sets to have our ``X_train``, ``y_train`` , ``X_test`` and ``y_test``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "60014960",
   "metadata": {
    "id": "60014960"
   },
   "outputs": [],
   "source": [
    "#Split the labels and target into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26e396f",
   "metadata": {
    "id": "a26e396f"
   },
   "source": [
    "Then instatiate our models in a list with the hyperparameters need for tuning them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4dea8f13",
   "metadata": {
    "id": "4dea8f13"
   },
   "outputs": [],
   "source": [
    "# Create a list containing the various models to evaluate\n",
    "# include the parameters we are interested to tune for each model\n",
    "models = [\n",
    "    ## define the model Support Vector Classifier\n",
    "    {  \n",
    "        'model': SVC(), # instantiate the model\n",
    "        'param_grid': { # set hyper parameters for model tuning\n",
    "            'C': [1, 10, 100],\n",
    "            'gamma': [0.1,0.01,0.001],\n",
    "            'kernel': ['rbf']\n",
    "        }\n",
    "    },\n",
    "    ## define the model Logistic Regression Classifier\n",
    "    {  \n",
    "        'model': LogisticRegression(), # instantiate the model\n",
    "        'param_grid': { # set hyper parameters for model tuning\n",
    "            'C' : [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "            'solver' : ['lbfgs', 'liblinear'],\n",
    "            'max_iter' : [100, 1000, 2500]\n",
    "        }\n",
    "    },\n",
    "    ## define the model KNeighbors Classifier\n",
    "    { \n",
    "        'model': KNeighborsClassifier(), # instantiate the model\n",
    "        'param_grid': { # set hyper parameters for model tuning\n",
    "            'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "        }\n",
    "    },\n",
    "    ## define the mode MultinomialNB Classifier\n",
    "    {\n",
    "        'model': MultinomialNB(), # instantiate the model\n",
    "        'param_grid': { # set hyper parameters for model tuning\n",
    "            'alpha': [0.0001, 0.001, 0.1, 1, 10]\n",
    "        }\n",
    "    },\n",
    "    ## define the model Random Forest Classifier\n",
    "    {  \n",
    "        'model': RandomForestClassifier(), # instantiate the model\n",
    "        'param_grid': { # set hyper parameters for model tuning\n",
    "            'max_depth': [10, 20, 40, 50, None],\n",
    "            'n_estimators': [200, 300, 400, 500]\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "509d634f",
   "metadata": {
    "id": "509d634f"
   },
   "source": [
    "Finally, let's create functions to fit the models and evaluate them..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50d39f9",
   "metadata": {
    "id": "f50d39f9"
   },
   "source": [
    "###### 1. function to fit the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "17d940f1",
   "metadata": {
    "id": "17d940f1"
   },
   "outputs": [],
   "source": [
    "# Function to fit the base models for on the data\n",
    "# to view their initial performance\n",
    "def fit_models(X_train, y_train, models):\n",
    "    models_list = []\n",
    "    for item in models:\n",
    "        model = item['model']\n",
    "        model.fit(X_train, y_train)\n",
    "        models_list.append((model.__class__.__name__, model))\n",
    "    return models_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3adf2efa",
   "metadata": {
    "id": "3adf2efa"
   },
   "source": [
    "###### 2. function to evaluate the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "abff9d9e",
   "metadata": {
    "id": "abff9d9e"
   },
   "outputs": [],
   "source": [
    "# Function to predict targets for X_test\n",
    "# and to return the accuracy and f1 scores, classification report, and confusion matrix\n",
    "def evaluate_models(X_test, y_test, models):\n",
    "    accuracy_scores = []\n",
    "    f1_scores = []\n",
    "    classification_reports = []\n",
    "    confusion_matrices = []\n",
    "\n",
    "    for (name, fitted_model) in fitted_models:\n",
    "        y_pred = fitted_model.predict(X_test)\n",
    "        accuracy_scores.append( # add to accuracy_scores the accuracy of model\n",
    "            accuracy_score(y_test, y_pred) # find the accuracy\n",
    "        )\n",
    "        f1_scores.append( # add to f1_scores the f1 score of model\n",
    "            f1_score(y_test, y_pred, average='weighted') # find the f1 score\n",
    "        )\n",
    "        classification_reports.append( # add to f1_scores the f1 score of model\n",
    "            (name.upper(), classification_report(y_test, y_pred))\n",
    "        )\n",
    "        confusion_matrices.append(\n",
    "            (name.upper(), confusion_matrix(y_test, y_pred))\n",
    "        )\n",
    "    return (accuracy_scores, f1_scores), (classification_reports, confusion_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56706b20",
   "metadata": {
    "id": "56706b20"
   },
   "source": [
    "Finally, having finished creating all those, let's proceed to training our models on the training data sets (``X_train``,``y_train``)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f8643cf7",
   "metadata": {
    "id": "f8643cf7"
   },
   "outputs": [],
   "source": [
    "# Fit the various model\n",
    "fitted_models = fit_models(X_train, y_train, models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b9558890",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b9558890",
    "outputId": "181f855b-e343-434c-f47a-ff280b2fda84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SVC', SVC()),\n",
       " ('LogisticRegression', LogisticRegression()),\n",
       " ('KNeighborsClassifier', KNeighborsClassifier()),\n",
       " ('MultinomialNB', MultinomialNB()),\n",
       " ('RandomForestClassifier', RandomForestClassifier())]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_models # View the trained models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a266d3",
   "metadata": {
    "id": "76a266d3"
   },
   "source": [
    "With our benchmark models trained, we need to evaluate their performance. We will use th skit learn's ``f1`` and ``accuracy`` scores to evaluate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "99f690f7",
   "metadata": {
    "id": "99f690f7"
   },
   "outputs": [],
   "source": [
    "# Now we predict the target for the X_test for the models\n",
    "# and get the accuracy and f1 scores\n",
    "(accuracy_scores, f1_scores), _ = evaluate_models(X_test, y_test, fitted_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04f6d38",
   "metadata": {
    "id": "a04f6d38"
   },
   "source": [
    "Let's view the scores of the models on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "tP_YaPtS_tMi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "tP_YaPtS_tMi",
    "outputId": "1788d932-ed78-4f41-ce6c-238ddac398db"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>model</th>\n",
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       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.91950</td>\n",
       "      <td>0.919429</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.884754</td>\n",
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      "text/plain": [
       "                    model  accuracy  f1 score\n",
       "0                     SVC   0.91950  0.919429\n",
       "1      LogisticRegression   0.88650  0.884754\n",
       "2    KNeighborsClassifier   0.75650  0.757100\n",
       "3           MultinomialNB   0.87050  0.871174\n",
       "4  RandomForestClassifier   0.91275  0.912660"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's create a Dataframe to tabulate the performance of various models\n",
    "base_models_performance_df = pd.DataFrame(\n",
    "    zip([i[0] for i in fitted_models], accuracy_scores, f1_scores),\n",
    "    columns = ['model', 'accuracy', 'f1 score']\n",
    ")\n",
    "# and view the output as a table\n",
    "base_models_performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330a422f",
   "metadata": {
    "id": "330a422f"
   },
   "source": [
    "From the default models, we can observe that the SVC and Random Forest are the best (with the SVC having a slight edge), while alsothe Multinomial and Logistic coming in close. Only the Kneighbours has score that is far lower that the other four models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253b03e0",
   "metadata": {
    "id": "253b03e0"
   },
   "source": [
    "## <font id='ModelT' color='goldenrod'>7.  Tuning the Hyper Parameter of Models</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d28d05",
   "metadata": {
    "id": "07d28d05"
   },
   "source": [
    "To finish the selection of our **best model**, we need to tune the models with the selected hyper parameter to see if we can get the best performing model. From these models we will then select the best scoring model with the best hyper parameters, using ``f1`` score.\n",
    "\n",
    "To do this we will create a function that we will use to tune the models, before finally evaluate their performance on ``f1`` score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b2e164c6",
   "metadata": {
    "id": "b2e164c6"
   },
   "outputs": [],
   "source": [
    "# Function to tune our base models\n",
    "# and find the optimal params for each of our model \n",
    "# We use the f1 score as our scoring criteria\n",
    "# and a cross-validation splitting (cv) of 10\n",
    "def tuned_models(X_train, y_train, models):\n",
    "    tuned_models = [] # initialise an empty list\n",
    "    for item in models:\n",
    "        model = item['model'] # get the model object instance\n",
    "        param_grid = item['param_grid'] # get the parameters that will be used for tuning\n",
    "        # define the grid instance\n",
    "        grid =  GridSearchCV(\n",
    "            model, # model instance\n",
    "            param_grid = param_grid, # hyper-parameters\n",
    "            cv = 10, # cross-validation splitting\n",
    "            scoring = 'f1_weighted', # f1_scoring\n",
    "            refit=True\n",
    "        )\n",
    "        print(f'Fitting {model.__class__.__name__} model....')\n",
    "        grid.fit(X_train, y_train) # fit the grid to the X-train and the y_train\n",
    "        tuned_models.append({ # add to tuned_models list the\n",
    "            'name': model.__class__.__name__, # model name\n",
    "            'tuned model': grid, # best model instance\n",
    "            'best params': grid.best_params_, # best model parameters\n",
    "            'f1 score': grid.best_score_, # best f1 score\n",
    "            'refit time': grid.refit_time_ # the time the model took to refit the data\n",
    "        })\n",
    "        print(f'Finished fitting {grid.best_estimator_}')\n",
    "        \n",
    "    return tuned_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4885bc3d",
   "metadata": {
    "id": "4885bc3d"
   },
   "source": [
    "Let's now tune our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1cf00a96",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1cf00a96",
    "outputId": "55378c9b-0431-4709-d438-4ebc6f80ba20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting SVC model....\n",
      "Finished fitting SVC(C=100, gamma=0.1)\n",
      "Fitting LogisticRegression model....\n",
      "Finished fitting LogisticRegression(C=100, max_iter=1000)\n",
      "Fitting KNeighborsClassifier model....\n",
      "Finished fitting KNeighborsClassifier(n_neighbors=1)\n",
      "Fitting MultinomialNB model....\n",
      "Finished fitting MultinomialNB(alpha=0.1)\n",
      "Fitting RandomForestClassifier model....\n",
      "Finished fitting RandomForestClassifier(n_estimators=400)\n"
     ]
    }
   ],
   "source": [
    "# Tuning the models' parameters to produce the best model estimators\n",
    "tuned_models = tuned_models(X_train, y_train, models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "lmdICT6EfkDA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "lmdICT6EfkDA",
    "outputId": "cbf54eef-e386-4d4a-8e4a-10c89c9393d9"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Best Params</th>\n",
       "      <th>f1 Score</th>\n",
       "      <th>Refit Time</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>{'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.901064</td>\n",
       "      <td>44.972516</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>{'C': 100, 'max_iter': 1000, 'solver': 'lbfgs'}</td>\n",
       "      <td>0.902581</td>\n",
       "      <td>22.511147</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>{'n_neighbors': 1}</td>\n",
       "      <td>0.856711</td>\n",
       "      <td>0.002073</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>{'alpha': 0.1}</td>\n",
       "      <td>0.894328</td>\n",
       "      <td>0.011622</td>\n",
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       "                    Model                                      Best Params  \\\n",
       "0                     SVC        {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "1      LogisticRegression  {'C': 100, 'max_iter': 1000, 'solver': 'lbfgs'}   \n",
       "2    KNeighborsClassifier                               {'n_neighbors': 1}   \n",
       "3           MultinomialNB                                   {'alpha': 0.1}   \n",
       "4  RandomForestClassifier         {'max_depth': None, 'n_estimators': 400}   \n",
       "\n",
       "   f1 Score  Refit Time  \n",
       "0  0.901064   44.972516  \n",
       "1  0.902581   22.511147  \n",
       "2  0.856711    0.002073  \n",
       "3  0.894328    0.011622  \n",
       "4  0.905612  135.853721  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's create a Dataframe to tabulate best estimators and their performance\n",
    "tuned_models_performance_df = pd.DataFrame(\n",
    "    [(model['name'], model['best params'], model['f1 score'], model['refit time']) for model in tuned_models],\n",
    "    columns = ['Model', 'Best Params', 'f1 Score', 'Refit Time'])\n",
    "# and view the output as a table\n",
    "tuned_models_performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bf9df0",
   "metadata": {
    "id": "29bf9df0"
   },
   "source": [
    "## <font id='ModelE' color='goldenrod'>8. Evaluating our Model Performance</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f370a001",
   "metadata": {
    "id": "f370a001"
   },
   "source": [
    "For the tuned models, let's view the f1 score of each tuned model. We then will selected the model with the overall best *f1 score* as our model and as evaluate its classification performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a019d5",
   "metadata": {
    "id": "c1a019d5"
   },
   "source": [
    "Continue with our best model SVC model, we are going to look at the confusion matrix, and show the discrepancies between predicted and actual labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "19858a93",
   "metadata": {
    "id": "19858a93"
   },
   "outputs": [],
   "source": [
    "model = SVC(C=100, gamma=0.1) # instatiate the final model\n",
    "model.fit(X_train, y_train) # train the model\n",
    "y_pred = model.predict(X_test) # let's predict the labels for our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0UfgcnxOn9rd",
   "metadata": {
    "colab": {
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     "height": 621
    },
    "id": "0UfgcnxOn9rd",
    "outputId": "4eb78547-478a-458b-f625-a489c2832587"
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred) # Create a confusion matrix of the model to show the predicted and actual values\n",
    "fig, ax = plt.subplots(figsize=(10,10)) # Create a sub plot for the a heatmap visualisation\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d',\n",
    "            cmap='BuGn',\n",
    "            xticklabels=df.sentiment.unique(),\n",
    "            yticklabels=df.sentiment.unique()) # create a heatmap visualisation of the confusion matrix\n",
    "plt.ylabel('Actual') # label the y axis\n",
    "plt.xlabel('Predicted') # label the x axis\n",
    "plt.title('Confusion Matrix for SVC (Actual Vs Predicted)')\n",
    "plt.show() # show plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "W8w0NdZlqke5",
   "metadata": {
    "id": "W8w0NdZlqke5"
   },
   "source": [
    "Also, lets view the classification report for our best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "Rg2eYStupg8v",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "id": "Rg2eYStupg8v",
    "outputId": "2ae46321-eaf8-4527-a8b5-bd554a7fe815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of SVC Model\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n          -1       0.97      0.98      0.98       977\\n           0       0.91      0.91      0.91      1004\\n           1       0.88      0.83      0.85       995\\n           2       0.91      0.95      0.93      1024\\n\\n    accuracy                           0.92      4000\\n   macro avg       0.92      0.92      0.92      4000\\nweighted avg       0.92      0.92      0.92      4000\\n'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classf_report = classification_report(y_test, y_pred)\n",
    "print('Classification Report of SVC Model')\n",
    "classf_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed36d14",
   "metadata": {
    "id": "fed36d14"
   },
   "source": [
    "## KAGGLE SUBMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c9f76ba6",
   "metadata": {
    "colab": {
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     "height": 455
    },
    "id": "c9f76ba6",
    "outputId": "d761ea29-8a51-4688-ab20-37a85de7b43b"
   },
   "outputs": [
    {
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       "                                                   message\n",
       "tweetid                                                   \n",
       "169760   Europe will now be looking to China to make su...\n",
       "35326    Combine this with the polling of staffers re c...\n",
       "224985   The scary, unimpeachable evidence that climate...\n",
       "476263   @Karoli @morgfair @OsborneInk @dailykos \\r\\nPu...\n",
       "872928   RT @FakeWillMoore: 'Female orgasms cause globa...\n",
       "...                                                    ...\n",
       "895714   RT @BrittanyBohrer: Brb, writing a poem about ...\n",
       "875167   2016: the year climate change came home: Durin...\n",
       "78329    RT @loop_vanuatu: Pacific countries positive a...\n",
       "867455   RT @xanria_00018: You’re so hot, you must be t...\n",
       "470892   RT @chloebalaoing: climate change is a global ...\n",
       "\n",
       "[10546 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('./data/test_with_no_labels.csv', index_col='tweetid')\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11be979a",
   "metadata": {
    "id": "11be979a"
   },
   "outputs": [],
   "source": [
    "test_tokenized = vectorizer.transform(test_df['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2T0w0WAXOwMW",
   "metadata": {
    "id": "2T0w0WAXOwMW"
   },
   "outputs": [],
   "source": [
    "test_trans = tfidf_transformer.fit_transform(test_tokenized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c84f0adb",
   "metadata": {
    "id": "c84f0adb"
   },
   "outputs": [],
   "source": [
    "for (fitted_name, fitted_model) in fitted_models:\n",
    "    test_pred = fitted_model.predict(test_trans)\n",
    "    results_df = pd.DataFrame(test_pred.tolist(), index = test_df.index, columns=['sentiment'])\n",
    "    results_df.to_csv(f\"{fitted_name}_classification_predict.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "db008038",
   "metadata": {
    "id": "db008038"
   },
   "outputs": [],
   "source": [
    "for tuned_model in tuned_models:\n",
    "    model_instance = tuned_model['tuned model']\n",
    "    test_pred = model_instance.predict(test_trans)\n",
    "    results_df = pd.DataFrame(test_pred.tolist(), index = test_df.index, columns=['sentiment'])\n",
    "    results_df.to_csv(f\"submissions/{tuned_model['name']}_classification_predict.csv\")"
   ]
  }
 ],
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