human_annotations_eda.ipynb

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   "source": [
    "This notebook creates visualizations of the human annotations for all the six NLP tasks.\n",
    "\n",
    "* Input: 'AgoraSpeech_preprocessed.csv'\n",
    "* Output: 'correlations.png', 'criticim_agenda.png', 'entities_horizontal_bars.png', 'entities_wordcloud_simple.png', 'entities_wordcloud.png', 'polarization.png', 'populism.png', 'sentiment.png', topics_grouped_bars.png', and 'topics_horizontal_bars.png'\n",
    "* Actions: It creates visualizations for answering the following questions (corresponds to the six NLP tasks):\n",
    "    1. Do politicians focus on presenting their agenda or on criticizing their opponents?\n",
    "    2. In what topics are politicians' campaign speeches focused on?\n",
    "    3. What is the prevalent sentiment in politicians’ speeches?\n",
    "    4. To what extent are politicians’ speeches polarized?\n",
    "    5. Do politicians’ speeches exhibit populism?\n",
    "    6. Do politicians often refer to specific entities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from wordcloud import WordCloud\n",
    "warnings.filterwarnings('ignore')\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.colors as mcolors\n",
    "from scipy.stats import spearmanr, chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "data = pd.read_csv('AgoraSpeech_preprocessed.csv')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Do politicians focus on presenting their agenda or on criticizing their opponents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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    "# prepare the data for the plot\n",
    "df_grouped = data.groupby(['politician', 'criticism_or_agenda_human']).size().reset_index(name='count')\n",
    "df_grouped['total_per_politician'] = df_grouped.groupby('politician')['count'].transform('sum')\n",
    "df_grouped['proportion'] = round(df_grouped['count'] / df_grouped['total_per_politician'],2)\n",
    "df_grouped.drop(columns=['count', 'total_per_politician'], inplace=True)\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'Mitsotakis': '#007dea', 'Tsipras': '#ef4393', 'Androulakis': '#00cc44', 'Koutsoumpas': '#ff4d4d', 'Velopoulos': '#5cb2e5', 'Varoufakis': '#fd9347'}\n",
    "fig = px.bar(df_grouped, x='criticism_or_agenda_human', y='proportion', color='politician', text='proportion', barmode='group', color_discrete_map=color_map,\n",
    "             labels={'proportion': 'Fraction of paragraphs', 'criticism_or_agenda_human': 'Criticim vs. Agenda'}, text_auto=True)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=14, family='Arial'))\n",
    "\n",
    "# plot the lines (overall statistics)\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=-0.45, x1=0.45, y0=0.39, y1=0.39,\n",
    "    line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "    xref=\"x\", yref=\"y\")\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0.55, x1=1.45, y0=0.60, y1=0.60,\n",
    "    line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "    xref=\"x\", yref=\"y\")\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=[None], y=[None],\n",
    "        mode='lines',\n",
    "        line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "        showlegend=True, name='Overall'))\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=1100, height=500,\n",
    "    legend_title_text='Politicians',\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.0, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "    tickfont=dict(size=16, family='Arial', color='black'))\n",
    "fig.update_yaxes( title_font=dict(size=18, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/criticism_agenda.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what topics are politicians' campaign speeches focused on?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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           "size": 18
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         }
        },
        "yaxis": {
         "anchor": "x",
         "categoryarray": [
          "migration",
          "pensioners",
          "privatization",
          "accountability",
          "national security",
          "agricultural policy",
          "external affairs",
          "corruption",
          "infrastructure",
          "energy",
          "social state",
          "education",
          "housing",
          "debt",
          "other",
          "democracy",
          "health",
          "employment",
          "economy",
          "elections"
         ],
         "categoryorder": "total ascending",
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   ],
   "source": [
    "# prepare the data for the plot\n",
    "topic_count = data['topic_human'].value_counts(normalize=True).reset_index()\n",
    "topic_count.columns = ['topic_human', 'count']\n",
    "topic_count['count'] = round(topic_count['count'], 3)\n",
    "topic_top20 = topic_count.head(20)\n",
    "\n",
    "# plot the horizontal bars\n",
    "cmap = mcolors.LinearSegmentedColormap.from_list('custom_blues', ['#4575b4', '#b5c9e3'], N=20)\n",
    "custom_colors = [mcolors.to_hex(cmap(i/19)) for i in range(20)]\n",
    "fig = px.bar(topic_top20, x='count', y='topic_human', orientation='h',\n",
    "             labels={'count': 'Fraction  of paragraphs ', 'topic_human': 'Topic'}, \n",
    "             color='topic_human', color_discrete_sequence=custom_colors, text='count')\n",
    "fig.update_layout(showlegend=False, yaxis={'categoryorder':'total ascending'}, width=1100, height=500)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=14, family='Arial'))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'))\n",
    "fig.update_yaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "                 tickfont=dict(size=14, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/topics_horizontal_bars.png\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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   "source": [
    "# prepare the data for the plot\n",
    "top_topics = data['topic_human'].value_counts().head(5).index\n",
    "filtered_data = data[data['topic_human'].isin(top_topics)]\n",
    "grouped_data = filtered_data.groupby(['politician', 'topic_human']).size().reset_index(name='count')\n",
    "grouped_data\n",
    "\n",
    "total_rows_per_politician = data.groupby('politician').size().reset_index(name='total_rows')\n",
    "grouped_data = pd.merge(grouped_data, total_rows_per_politician, on='politician')\n",
    "grouped_data['normalized_count'] = round(grouped_data['count']/grouped_data['total_rows'],2)\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'Mitsotakis': '#007dea', 'Tsipras': '#ef4393', 'Androulakis': '#00cc44', 'Koutsoumpas': '#ff4d4d', 'Velopoulos': '#5cb2e5', 'Varoufakis': '#fd9347'}\n",
    "\n",
    "fig = px.bar(grouped_data, x='topic_human', y='normalized_count', color='politician', text='normalized_count', barmode='group', color_discrete_map=color_map,\n",
    "             labels={'normalized_count': 'Fraction of paragraphs', 'topic_human': 'Topic'}, category_orders={'topic_human': ['elections', 'economy', 'employment']}, text_auto=True)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=14, family='Arial'))\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(width=1100, height=500,legend_title_text='Politicians',\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.0, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "    tickfont=dict(size=16, family='Arial', color='black'))\n",
    "fig.update_yaxes( title_font=dict(size=18, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/topics_grouped_bars.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the prevalent sentiment in politicians' speeches?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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    "# prepare the data for the plot\n",
    "df_grouped = data.groupby(['politician', 'sentiment_human_category']).size().reset_index(name='count')\n",
    "df_grouped['total_per_politician'] = df_grouped.groupby('politician')['count'].transform('sum')\n",
    "df_grouped['proportion'] = round(df_grouped['count'] / df_grouped['total_per_politician'],2)\n",
    "df_grouped.drop(columns=['count', 'total_per_politician'], inplace=True)\n",
    "\n",
    "# calculate the overall statistics\n",
    "overall_sentiment = data.groupby('sentiment_human_category').size().reset_index(name='count')\n",
    "overall_sentiment['total'] = overall_sentiment['count'].sum()\n",
    "overall_sentiment['proportion'] = round(overall_sentiment['count'] / overall_sentiment['total'], 2)\n",
    "\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'Overall': '#666666','Mitsotakis': '#007dea', 'Tsipras': '#ef4393', 'Androulakis': '#00cc44', 'Koutsoumpas': '#ff4d4d', 'Velopoulos': '#5cb2e5', 'Varoufakis': '#fd9347'}\n",
    "fig = px.bar(df_grouped, x='sentiment_human_category', y='proportion', color='politician', text='proportion', barmode='group', color_discrete_map=color_map,\n",
    "             labels={'proportion': 'Fraction of paragraphs', 'sentiment_human_category': 'Sentiment level'}, text_auto=True)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=14, family='Arial'))\n",
    "\n",
    "# plot the lines (overall statistics)\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=-0.45, x1=0.45, y0=overall_sentiment['proportion'].iloc[0], y1=overall_sentiment['proportion'].iloc[0],\n",
    "    line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "    xref=\"x\", yref=\"y\")\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0.55, x1=1.45, y0=overall_sentiment['proportion'].iloc[1], y1=overall_sentiment['proportion'].iloc[1],\n",
    "    line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "    xref=\"x\", yref=\"y\")\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=1.55, x1=2.45, y0=overall_sentiment['proportion'].iloc[2], y1=overall_sentiment['proportion'].iloc[2],\n",
    "    line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "    xref=\"x\", yref=\"y\")\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=[None], y=[None],\n",
    "        mode='lines',\n",
    "        line=dict(color=\"#666666\", width=3, dash=\"dash\"),\n",
    "        showlegend=True, name='Overall'))\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=1100, height=500,\n",
    "    legend_title_text='Politicians',\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.0, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "    tickfont=dict(size=16, family='Arial', color='black'))\n",
    "fig.update_yaxes( title_font=dict(size=18, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/sentiment.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To what extent are politicians' speeches polarized?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_proportions(group):\n",
    "    group['proportion'] = group['proportion'].round(2)\n",
    "    diff = 1.00 - group['proportion'].sum()\n",
    "    if diff != 0:\n",
    "        group.loc[group['proportion'].idxmax(), 'proportion'] += diff\n",
    "    return group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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   ],
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    "# prepare the data for the plot\n",
    "df_grouped = data.groupby(['politician', 'polarization_human_category']).size().reset_index(name='count')\n",
    "df_grouped['total_per_politician'] = df_grouped.groupby('politician')['count'].transform('sum')\n",
    "df_grouped['proportion'] = df_grouped['count']/df_grouped['total_per_politician']\n",
    "df_grouped = df_grouped.groupby('politician').apply(adjust_proportions)\n",
    "df_grouped.drop(columns=['count', 'total_per_politician'], inplace=True)\n",
    "\n",
    "# plot the stacked bars\n",
    "color_map = {'low': '#74add1', 'medium': '#fee090', 'high': '#f46d43'}\n",
    "fig = px.bar(df_grouped, x='politician', y='proportion', color='polarization_human_category',\n",
    "             barmode='stack', color_discrete_map=color_map, text='proportion',\n",
    "             labels={'proportion': 'Fraction of paragraphs', 'politician': 'Politician'},\n",
    "             category_orders={'polarization_human_category': ['low', 'medium', 'high']}, text_auto=True)\n",
    "fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')\n",
    "fig.update_traces(textposition='inside', textfont=dict(size=12, family='Arial'))\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=750, height=500,\n",
    "    legend_title_text='Polarization level',\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.01, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "    tickfont=dict(size=16, family='Arial', color='black'))\n",
    "fig.update_yaxes(title_font=dict(size=18, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/polarization.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do politicians' speeches exhibit populism?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "uniformtext": {
         "minsize": 12,
         "mode": "hide"
        },
        "width": 750,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "tickfont": {
          "color": "black",
          "family": "Arial",
          "size": 16
         },
         "title": {
          "font": {
           "color": "black",
           "family": "Arial",
           "size": 18
          },
          "text": "Politician"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "font": {
           "color": "black",
           "family": "Arial",
           "size": 18
          },
          "text": "Fraction of paragraphs"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the data for the plot\n",
    "df_grouped = data.groupby(['politician', 'populism_human_category']).size().reset_index(name='count')\n",
    "df_grouped['total_per_politician'] = df_grouped.groupby('politician')['count'].transform('sum')\n",
    "df_grouped['proportion'] = df_grouped['count']/df_grouped['total_per_politician']\n",
    "df_grouped = df_grouped.groupby('politician').apply(adjust_proportions)\n",
    "df_grouped.drop(columns=['count', 'total_per_politician'], inplace=True)\n",
    "\n",
    "# plot the stacked bars\n",
    "color_map = {'low': '#74add1', 'medium': '#fee090', 'high': '#f46d43'}\n",
    "fig = px.bar(df_grouped, x='politician', y='proportion', color='populism_human_category',\n",
    "             barmode='stack', color_discrete_map=color_map, text='proportion',\n",
    "             labels={'proportion': 'Fraction of paragraphs', 'politician': 'Politician'},\n",
    "             category_orders={'populism_human_category': ['low', 'medium', 'high']}, text_auto=True)\n",
    "fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')\n",
    "fig.update_traces(textposition='inside', textfont=dict(size=12, family='Arial'))\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=750, height=500,\n",
    "    legend_title_text='Populism level',\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.01, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "    tickfont=dict(size=16, family='Arial', color='black'))\n",
    "fig.update_yaxes(title_font=dict(size=18, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/populism.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do politicians often refer to specific entities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "data['entity_specific_human'] = data['entity_specific_human'].apply(lambda x: ast.literal_eval(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the data for the plot\n",
    "all_entities = [entity for sublist in data['entity_specific_human'] for entity in sublist]\n",
    "entity_counts = Counter(all_entities)\n",
    "top_20_entities = entity_counts.most_common(50)\n",
    "word_freq = dict(top_20_entities)\n",
    "\n",
    "# create the color mapping\n",
    "cmap = mcolors.LinearSegmentedColormap.from_list('custom_blues', ['#4678b9', '#1c304a'], N=50)\n",
    "custom_colors = [mcolors.to_hex(cmap(i/49)) for i in range(50)]\n",
    "min_freq = min(word_freq.values())\n",
    "max_freq = max(word_freq.values())\n",
    "def color_func(word, font_size, position, orientation, random_state=None, **kwargs):\n",
    "    frequency = word_freq[word]\n",
    "    normalized_freq = (frequency - min_freq) / (max_freq - min_freq)\n",
    "    r, g, b, a = cmap(normalized_freq)\n",
    "    return f\"rgb({int(r*255)}, {int(g*255)}, {int(b*255)})\"\n",
    "\n",
    "# plot the wordcloud\n",
    "wordcloud = WordCloud(width=200, height=250, background_color=\"white\").generate_from_frequencies(word_freq)\n",
    "wordcloud = wordcloud.recolor(color_func=color_func)\n",
    "\n",
    "# save with high resolution\n",
    "plt.figure(figsize=(2,1), dpi=300)\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.savefig(\"images/entities_wordcloud_simple.png\", dpi=300, bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "legendgroup": "Androulakis",
         "marker": {
          "color": "#00cc44",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Androulakis",
         "offsetgroup": "Androulakis",
         "orientation": "h",
         "showlegend": true,
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         "yaxis": "y"
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        {
         "alignmentgroup": "True",
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         "legendgroup": "Koutsoumpas",
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         },
         "name": "Koutsoumpas",
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         "legendgroup": "Mitsotakis",
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        {
         "alignmentgroup": "True",
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         "legendgroup": "Tsipras",
         "marker": {
          "color": "#ef4393",
          "pattern": {
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          }
         },
         "name": "Tsipras",
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         "alignmentgroup": "True",
         "hovertemplate": "Politician=Varoufakis<br>Occurrence of entities=%{x}<br>Entity=%{y}<extra></extra>",
         "legendgroup": "Varoufakis",
         "marker": {
          "color": "#fd9347",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Varoufakis",
         "offsetgroup": "Varoufakis",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
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         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Politician=Velopoulos<br>Occurrence of entities=%{x}<br>Entity=%{y}<extra></extra>",
         "legendgroup": "Velopoulos",
         "marker": {
          "color": "#5cb2e5",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Velopoulos",
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       "layout": {
        "barmode": "stack",
        "height": 500,
        "legend": {
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         "orientation": "h",
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         "y": 1,
         "yanchor": "bottom"
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        "yaxis": {
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   "source": [
    "# prepare the data for the plot\n",
    "expanded_data = data.explode('entity_specific_human')\n",
    "entity_politician_count = expanded_data.groupby(['entity_specific_human', 'politician']).size().reset_index(name='count')\n",
    "top_20_entities = expanded_data['entity_specific_human'].value_counts().nlargest(20).index\n",
    "entity_politician_count = entity_politician_count[entity_politician_count['entity_specific_human'].isin(top_20_entities)]\n",
    "entity_total_count = entity_politician_count.groupby('entity_specific_human')['count'].sum().reset_index(name='total_count')\n",
    "sorted_entities = entity_total_count.sort_values(by='total_count', ascending=False)['entity_specific_human'].to_list()\n",
    "\n",
    "# plot the horizontal bars\n",
    "color_map = {'Overall': '#666666','Mitsotakis': '#007dea', 'Tsipras': '#ef4393', 'Androulakis': '#00cc44', 'Koutsoumpas': '#ff4d4d', 'Velopoulos': '#5cb2e5', 'Varoufakis': '#fd9347'}\n",
    "fig = px.bar(entity_politician_count, x='count', y='entity_specific_human',  barmode='stack',\n",
    "             orientation='h', color='politician', color_discrete_map=color_map, category_orders={'entity_specific_human': sorted_entities},\n",
    "             labels={'count': 'Occurrence of entities', 'entity_specific_human': 'Entity', 'politician': 'Politician'})\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=1000, height=500,\n",
    "    legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.0, xanchor=\"center\", x=0.5, font=dict(size=16, family='Arial')))\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(title_font=dict(size=18, family='Arial', color='black'))\n",
    "fig.update_yaxes(title_font=dict(size=18, family='Arial', color='black'),\n",
    "                 tickfont=dict(size=14, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/entities_horizontal_bars.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CORRELATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cramers_v(x, y):\n",
    "    confusion_matrix = pd.crosstab(x, y)\n",
    "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
    "    n = confusion_matrix.sum().sum()\n",
    "    r, k = confusion_matrix.shape\n",
    "    return np.sqrt(chi2 / (n * (min(r, k) - 1)))"
   ]
  },
  {
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   "execution_count": 15,
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   "source": [
    "# prepare the data for the plot\n",
    "data_correlations = data[['criticism_or_agenda_human', 'topic_human', 'sentiment_human', 'polarization_human', 'populism_human', 'entity_specific_human']]\n",
    "data_correlations.rename(columns={'criticism_or_agenda_human': 'criticism or agenda', 'topic_human': 'topic', 'sentiment_human': 'sentiment', \n",
    "                                  'polarization_human': 'polarization', 'populism_human': 'populism', 'entity_specific_human': 'entity'}, inplace=True)\n",
    "data_correlations['entity'] = data_correlations['entity'].apply(lambda x: len(x)) # applied: entities count | alternatives: frequent specifc entities or category entities\n",
    "data_correlations['criticism or agenda'] = data_correlations['criticism or agenda'].map({'criticism': 0, 'political agenda': 1})\n",
    "\n",
    "numericals = ['criticism or agenda', 'sentiment', 'polarization', 'populism', 'entity']\n",
    "correlation_matrix = pd.DataFrame(index=data_correlations.columns, columns=data_correlations.columns)\n",
    "\n",
    "# between numerical columns\n",
    "for col1 in numericals:\n",
    "    for col2 in numericals:\n",
    "        corr, _ = spearmanr(data_correlations[col1], data_correlations[col2], nan_policy='omit')\n",
    "        correlation_matrix.loc[col1, col2] = corr\n",
    "\n",
    "# between numerical and the topic_human categorical column\n",
    "for col in numericals:  # convert numerical to categorical with quartiles\n",
    "    if col in ['sentiment', 'polarization', 'populism']:\n",
    "        data_correlations[col] = pd.qcut(data_correlations[col], 3, labels=False, duplicates='drop')\n",
    "    elif col == 'criticism or agenda':\n",
    "        data_correlations[col] = pd.qcut(data_correlations[col], 2, labels=False, duplicates='drop')\n",
    "    elif col == 'entity':\n",
    "        data_correlations[col] = pd.qcut(data_correlations[col], 33, labels=False, duplicates='drop')\n",
    "    correlation_matrix.loc[col, 'topic'] = cramers_v(data_correlations[col], data_correlations['topic'])\n",
    "correlation_matrix.loc['topic', 'topic'] = 1\n",
    "\n",
    "# data for the plot\n",
    "correlation_matrix = correlation_matrix.astype(float)\n",
    "mask = np.triu(np.ones(correlation_matrix.shape, dtype=bool), k=1)\n",
    "correlation_matrix = correlation_matrix.mask(mask).round(2)\n",
    "\n",
    "# plot the correlation matrix\n",
    "custom_colors = [(0.0, \"#d73027\" ), (0.25, \"#fdae61\"), (0.5, \"#ffffbf\"), (0.75, \"#abd9e9\"), (1.0, \"#4575b4\")]\n",
    "fig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale=custom_colors, aspect='auto', zmin=-1, zmax=1)\n",
    "fig.update_traces(textfont_size=14)\n",
    "\n",
    "# adjust figure size\n",
    "fig.update_layout( width=1100, height=500, xaxis_showgrid=False, yaxis_showgrid=False)\n",
    "\n",
    "# adjust axis\n",
    "fig.update_xaxes(tickfont=dict(size=15, family='Arial', color='black'))\n",
    "fig.update_yaxes(tickfont=dict(size=15, family='Arial', color='black'))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"images/correlations.png\", scale=2)"
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