chatgpt_annotations_eda.ipynb

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    "This notebook creates visualizations of the ChatGPT annotations in comparison to human annotations, for all the six NLP tasks.\n",
    "\n",
    "* Input: 'AgoraSpeech_preprocessed.csv'\n",
    "* Output: 'criticim_agenda_gpt.png', 'entities_gpt.png', 'polarization_gpt.png', 'populism_gpt.png', 'sentiment_gpt.png', and topics_gpt.png'\n",
    "* Actions: It creates visualizations for for comparing the ChatGPT vs human annotations for the NLP tasks:\n",
    "    1. Text classification: criticism vs. political agenda\n",
    "    2. Topic classification: from a predefined list of topics (https://lab.imedd.org/en/pos-analyoume-tis-proeklogikes-omilies-ton-politikon-archigon/)\n",
    "    3. Sentiment analysis: classification into negative, neutral, and positive\n",
    "    4. Polarization detection: classification into low, medium, and high\n",
    "    5. Populism detection: classification into low, medium, and high"
   ]
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  {
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    "import warnings\n",
    "import pandas as pd\n",
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    "# prepare the data for the plot\n",
    "df_melted = data[['criticism_or_agenda_gpt', 'criticism_or_agenda_human']].melt(var_name='feature', value_name='value')\n",
    "df_grouped = df_melted.groupby(['feature', 'value']).size().reset_index(name='count')\n",
    "totals = df_grouped.groupby('feature')['count'].sum().reset_index()\n",
    "totals['value'] = 'total'\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'criticism_or_agenda_gpt': '#fdbf81', 'criticism_or_agenda_human': '#abd9e9'}\n",
    "fig = px.bar(df_grouped, x='value', y='count', color='feature', text='count', barmode='group',\n",
    "    labels={'count': 'Count of paragraphs', 'value': 'Criticism vs. Agenda'}, text_auto=True, color_discrete_map=color_map)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=16, family='Arial'))\n",
    "\n",
    "# add the lines for the totals\n",
    "for index, row in totals.iterrows():\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=-0.45,  \n",
    "        x1=1.35,\n",
    "        y0=row['count'], y1=row['count'],\n",
    "        xref='x', yref='y',\n",
    "        line=dict(color=color_map[row['feature']], width=4, dash='dash'),\n",
    "        name=f'Total {row[\"feature\"]}')\n",
    "    fig.add_annotation(\n",
    "        x=1.35,\n",
    "        y=row['count'] + (index * 100),\n",
    "        text=f'Total: {row[\"count\"]}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=14, family='Arial', color='black'),\n",
    "        xanchor='left',\n",
    "        yanchor='middle')\n",
    "\n",
    "# adjust legend and figure size\n",
    "custom_labels = {'criticism_or_agenda_gpt': 'ChatGPT', 'criticism_or_agenda_human': 'Human'}\n",
    "fig.for_each_trace(lambda t: t.update(name=custom_labels[t.name]))\n",
    "fig.update_layout(\n",
    "    width=700, height=500,\n",
    "    legend_title_text='Annotations',\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=None)\n",
    "fig.update_xaxes(tickfont=dict(size=18, 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_gpt.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Topic classification: from a predefined list of topics (https://lab.imedd.org/en/pos-analyoume-tis-proeklogikes-omilies-ton-politikon-archigon/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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    "# prepare the data\n",
    "topic_counts_human = data['topic_human'].value_counts().reset_index().rename(columns={'index': 'topic', 'topic_human': 'topic'})\n",
    "topic_counts_human['column'] = 'topic_human'\n",
    "topic_counts_gpt = data['topic_gpt'].value_counts().reset_index().rename(columns={'index': 'topic', 'topic_gpt': 'topic'})\n",
    "topic_counts_gpt['column'] = 'topic_gpt'\n",
    "df_counts = pd.concat([topic_counts_gpt, topic_counts_human], ignore_index=True)\n",
    "top_5_topics = df_counts.groupby('topic')['count'].sum().nlargest(8).index\n",
    "df_counts_filtered = df_counts[df_counts['topic'].isin(top_5_topics)]\n",
    "df_counts_filtered['column'] = df_counts_filtered['column'].replace({'topic_gpt': 'ChatGPT', 'topic_human': 'Human'})\n",
    "totals_full_df = pd.concat([topic_counts_human, topic_counts_gpt], ignore_index=True).groupby('column')['count'].sum().reset_index()\n",
    "\n",
    "# plot the stacked bars\n",
    "custom_colors = ['#253494', '#225ea8', '#1d91c0', '#41b6c4', '#7fcdbb', '#c7e9b4', '#edf8b1', '#ffffd9']\n",
    "fig = px.bar(df_counts_filtered, x='column', y='count', color='topic', labels={'count': 'Count of paragraphs'},  color_discrete_sequence=custom_colors)\n",
    "\n",
    "# add the lines for the sum\n",
    "color_map = {'topic_gpt': '#fdbf81', 'topic_human': '#abd9e9'}\n",
    "for i, row in totals_full_df.iterrows():\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=i - 0.4,\n",
    "        x1=i + 0.4,\n",
    "        y0=row['count'],\n",
    "        y1=row['count'],\n",
    "        line=dict(color=color_map[row['column']], width=4, dash='dash'))\n",
    "    fig.add_annotation(\n",
    "        x=i,\n",
    "        y=row['count'] + 5,\n",
    "        text=f'Total: {row[\"count\"]}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=14, family='Arial', color='black'),\n",
    "        xanchor='center',\n",
    "        yanchor='bottom')\n",
    "\n",
    "# adjust legend and figure size\n",
    "fig.update_layout(\n",
    "    width=700, height=500,\n",
    "    legend_title_text='Topics',\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=None)\n",
    "fig.update_xaxes(tickfont=dict(size=18, 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_gpt.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sentiment analysis: classification into negative, neutral, and positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     },
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    "# prepare the data for the plot\n",
    "df_melted = data[['sentiment_gpt_category', 'sentiment_human_category']].melt(var_name='feature', value_name='value')\n",
    "df_grouped = df_melted.groupby(['feature', 'value']).size().reset_index(name='count')\n",
    "totals = df_grouped.groupby('feature')['count'].sum().reset_index()\n",
    "totals['value'] = 'total'\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'sentiment_gpt_category': '#fdbf81', 'sentiment_human_category': '#abd9e9'}\n",
    "fig = px.bar(df_grouped, x='value', y='count', color='feature', text='count', barmode='group',\n",
    "    labels={'count': 'Count of paragraphs', 'value': 'Sentiment'}, text_auto=True, color_discrete_map=color_map)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=16, family='Arial'))\n",
    "\n",
    "# add the lines for the totals\n",
    "for index, row in totals.iterrows():\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=-0.45,  \n",
    "        x1=2.35,\n",
    "        y0=row['count'], y1=row['count'],\n",
    "        xref='x', yref='y',\n",
    "        line=dict(color=color_map[row['feature']], width=4, dash='dash'),\n",
    "        name=f'Total {row[\"feature\"]}')\n",
    "    fig.add_annotation(\n",
    "        x=2.35,\n",
    "        y=row['count'],\n",
    "        text=f'Total: {row[\"count\"]}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=14, family='Arial', color='black'),\n",
    "        xanchor='left',\n",
    "        yanchor='middle')\n",
    "\n",
    "# adjust legend and figure size\n",
    "custom_labels = {'sentiment_gpt_category': 'ChatGPT', 'sentiment_human_category': 'Human'}\n",
    "fig.for_each_trace(lambda t: t.update(name=custom_labels[t.name]))\n",
    "fig.update_layout(\n",
    "    width=700, height=500,\n",
    "    legend_title_text='Annotations',\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=None)\n",
    "fig.update_xaxes(tickfont=dict(size=18, 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_gpt.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polarization detection: classification into low, medium, and high"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     },
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   ],
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    "# prepare the data for the plot\n",
    "df_melted = data[['polarization_gpt_category', 'polarization_human_category']].melt(var_name='feature', value_name='value')\n",
    "df_grouped = df_melted.groupby(['feature', 'value']).size().reset_index(name='count')\n",
    "totals = df_grouped.groupby('feature')['count'].sum().reset_index()\n",
    "totals['value'] = 'total'\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'polarization_gpt_category': '#fdbf81', 'polarization_human_category': '#abd9e9'}\n",
    "fig = px.bar(df_grouped, x='value', y='count', color='feature', text='count', barmode='group',\n",
    "             category_orders={'value': ['low', 'medium', 'high']},\n",
    "             labels={'count': 'Count of paragraphs', 'value': 'Sentiment'}, text_auto=True, color_discrete_map=color_map)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=16, family='Arial'))\n",
    "\n",
    "# add the lines for the totals\n",
    "for index, row in totals.iterrows():\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=-0.45,  \n",
    "        x1=2.35,\n",
    "        y0=row['count'], y1=row['count'],\n",
    "        xref='x', yref='y',\n",
    "        line=dict(color=color_map[row['feature']], width=4, dash='dash'),\n",
    "        name=f'Total {row[\"feature\"]}')\n",
    "    fig.add_annotation(\n",
    "        x=2.35,\n",
    "        y=row['count'] + (index * 150),\n",
    "        text=f'Total: {row[\"count\"]}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=14, family='Arial', color='black'),\n",
    "        xanchor='left',\n",
    "        yanchor='middle')\n",
    "\n",
    "# adjust legend and figure size\n",
    "custom_labels = {'polarization_gpt_category': 'ChatGPT', 'polarization_human_category': 'Human'}\n",
    "fig.for_each_trace(lambda t: t.update(name=custom_labels[t.name]))\n",
    "fig.update_layout(\n",
    "    width=700, height=500,\n",
    "    legend_title_text='Annotations',\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=None)\n",
    "fig.update_xaxes(tickfont=dict(size=18, 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_gpt.png\", scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Populism detection: classification into low, medium, and high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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        "width": 700,
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         "title": {
          "font": {
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          "text": "Count of paragraphs"
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       }
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     },
     "metadata": {},
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   ],
   "source": [
    "# prepare the data for the plot\n",
    "df_melted = data[['populism_gpt_category', 'populism_human_category']].melt(var_name='feature', value_name='value')\n",
    "df_grouped = df_melted.groupby(['feature', 'value']).size().reset_index(name='count')\n",
    "totals = df_grouped.groupby('feature')['count'].sum().reset_index()\n",
    "totals['value'] = 'total'\n",
    "\n",
    "# plot the grouped bars\n",
    "color_map = {'populism_gpt_category': '#fdbf81', 'populism_human_category': '#abd9e9'}\n",
    "fig = px.bar(df_grouped, x='value', y='count', color='feature', text='count', barmode='group',\n",
    "             category_orders={'value': ['low', 'medium', 'high']},\n",
    "             labels={'count': 'Count of paragraphs', 'value': 'Sentiment'}, text_auto=True, color_discrete_map=color_map)\n",
    "fig.update_traces(textposition='outside', textfont=dict(size=16, family='Arial'))\n",
    "\n",
    "# add the lines for the totals\n",
    "for index, row in totals.iterrows():\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=-0.45,  \n",
    "        x1=2.35,\n",
    "        y0=row['count'], y1=row['count'],\n",
    "        xref='x', yref='y',\n",
    "        line=dict(color=color_map[row['feature']], width=4, dash='dash'),\n",
    "        name=f'Total {row[\"feature\"]}')\n",
    "    fig.add_annotation(\n",
    "        x=2.35,\n",
    "        y=row['count'] + (index * 150),\n",
    "        text=f'Total: {row[\"count\"]}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=14, family='Arial', color='black'),\n",
    "        xanchor='left',\n",
    "        yanchor='middle')\n",
    "\n",
    "# adjust legend and figure size\n",
    "custom_labels = {'populism_gpt_category': 'ChatGPT', 'populism_human_category': 'Human'}\n",
    "fig.for_each_trace(lambda t: t.update(name=custom_labels[t.name]))\n",
    "fig.update_layout(\n",
    "    width=700, height=500,\n",
    "    legend_title_text='Annotations',\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=None)\n",
    "fig.update_xaxes(tickfont=dict(size=18, 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_gpt.png\", scale=2)"
   ]
  }
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