analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from tabulate import tabulate\n",
    "\n",
    "from IPython.display import display\n",
    "from scipy.stats import ttest_ind, chisquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# subjects in condition 'control': 15\n",
      "# subjects in condition '2_2_3': 15\n"
     ]
    }
   ],
   "source": [
    "exclude = [\n",
    "    \"abea6e2492.json\", # dummy file created to prevent participants from using a URL accidentally sent to multiple people\n",
    "    \"mara_test.json\" # test file\n",
    "]\n",
    "\n",
    "#control results\n",
    "cPath = 'survey_results/control'\n",
    "cFiles = [(cPath + '/' + pj) for pj in os.listdir(cPath) if pj not in exclude]\n",
    "cResults = []\n",
    "for f in cFiles:\n",
    "    with open(f) as of:\n",
    "        results_dict = json.load(of)\n",
    "        if results_dict.get(\"post_survey\"):\n",
    "            cResults.append(results_dict)\n",
    "print(f\"# subjects in condition 'control': {len(cResults)}\")\n",
    "\n",
    "#treatment results\n",
    "tPath = 'survey_results/2_2_3'\n",
    "tFiles = [(tPath + '/' + pj) for pj in os.listdir(tPath) if pj not in exclude]\n",
    "tResults = []\n",
    "for f in tFiles:\n",
    "    with open(f) as of:\n",
    "        results_dict = json.load(of)\n",
    "        if results_dict.get(\"post_survey\"):\n",
    "            tResults.append(results_dict)\n",
    "print(f\"# subjects in condition '2_2_3': {len(tResults)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "preSurveyItems = list(cResults[0]['pre_survey'].keys())\n",
    "cPreSurvey = {}\n",
    "tPreSurvey = {}\n",
    "for item in preSurveyItems:\n",
    "    cPreSurvey[item] = [r['pre_survey'][item] for r in cResults]\n",
    "    tPreSurvey[item] = [r['pre_survey'][item] for r in tResults]\n",
    "\n",
    "postSurveyItems = list(cResults[0]['post_survey'].keys())\n",
    "cPostSurvey = {}\n",
    "cOpinion = []\n",
    "tPostSurvey = {}\n",
    "tOpinion = []\n",
    "for item in postSurveyItems:\n",
    "    r1 = [r['post_survey'][item] for r in cResults]\n",
    "    r2 = [r['post_survey'][item] for r in tResults]\n",
    "    if 'statements' in item:\n",
    "        cOpinion = r1\n",
    "        tOpinion = r2\n",
    "    else:\n",
    "        cPostSurvey[item] = r1\n",
    "        tPostSurvey[item] = r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average control group digital literacy: 4.733 (± 0.458)\n",
      "Average control group chatbot experience: 3.867 (± 0.915)\n",
      "Average control group chatbot experience quality: 3.467 (± 0.834)\n",
      "Average 2x2x3 group digital literacy: 4.467 (± 0.640)\n",
      "Average 2x2x3 group chatbot experience: 4.333 (± 0.488)\n",
      "Average 2x2x3 group chatbot experience quality: 3.133 (± 0.915)\n"
     ]
    }
   ],
   "source": [
    "cPreSurvey_df = pd.DataFrame.from_dict(cPreSurvey)\n",
    "tPreSurvey_df = pd.DataFrame.from_dict(tPreSurvey)\n",
    "\n",
    "cPostSurvey_df = pd.DataFrame.from_dict(cPostSurvey)\n",
    "tPostSurvey_df = pd.DataFrame.from_dict(tPostSurvey)\n",
    "\n",
    "cOpinion_df = pd.DataFrame(cOpinion, columns=[\"Opinion (control)\"])\n",
    "tOpinion_df = pd.DataFrame(tOpinion, columns=[\"Opinion (2x2x3)\"])\n",
    "\n",
    "print(f\"Average control group digital literacy: {cPreSurvey_df['digital_literacy'].mean():.3f} (± {cPreSurvey_df['digital_literacy'].std():.3f})\")\n",
    "print(f\"Average control group chatbot experience: {cPreSurvey_df['prior_chatbot_has_experience'].mean():.3f} (± {cPreSurvey_df['prior_chatbot_has_experience'].std():.3f})\")\n",
    "print(f\"Average control group chatbot experience quality: {cPreSurvey_df['prior_chatbot_good_experience'].mean():.3f} (± {cPreSurvey_df['prior_chatbot_good_experience'].std():.3f})\")\n",
    "\n",
    "print(f\"Average 2x2x3 group digital literacy: {tPreSurvey_df['digital_literacy'].mean():.3f} (± {tPreSurvey_df['digital_literacy'].std():.3f})\")\n",
    "print(f\"Average 2x2x3 group chatbot experience: {tPreSurvey_df['prior_chatbot_has_experience'].mean():.3f} (± {tPreSurvey_df['prior_chatbot_has_experience'].std():.3f})\")\n",
    "print(f\"Average 2x2x3 group chatbot experience quality: {tPreSurvey_df['prior_chatbot_good_experience'].mean():.3f} (± {tPreSurvey_df['prior_chatbot_good_experience'].std():.3f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indices of outliers detected in condition 'control': [7] (total: 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indices of outliers detected in condition '2x2x3': [10] (total: 1)\n"
     ]
    }
   ],
   "source": [
    "outlier_detection = True\n",
    "outlier_quantile_threshold = 0.95\n",
    "if outlier_detection:\n",
    "    import hdbscan\n",
    "\n",
    "    hdbscan_control = hdbscan.HDBSCAN().fit(cPostSurvey_df.to_numpy().astype(np.float64))\n",
    "    outlier_threshold_control = pd.Series(hdbscan_control.outlier_scores_).quantile(outlier_quantile_threshold)\n",
    "    outliers_control = np.where(hdbscan_control.outlier_scores_ > outlier_threshold_control)[0]\n",
    "\n",
    "    cOutlierFilter = hdbscan_control.outlier_scores_ <= outlier_threshold_control\n",
    "    cPreSurvey_df = cPreSurvey_df[cOutlierFilter]\n",
    "    cPostSurvey_df = cPostSurvey_df[cOutlierFilter]\n",
    "    cOpinion_df = cOpinion_df[cOutlierFilter]\n",
    "    cOpinion = list(cOpinion_df[cOpinion_df.columns[0]])\n",
    "\n",
    "    sns.set(rc={'figure.figsize':(6,3)})\n",
    "    ax = plt.subplots()\n",
    "    ax = sns.barplot(x=list(range(len(hdbscan_control.outlier_scores_))), y=hdbscan_control.outlier_scores_)\n",
    "    ax.set_xlabel(\"Participant (Control group)\")\n",
    "    ax.set_ylabel(\"GLOSH outlier score\")\n",
    "    ax.axhline(outlier_threshold_control)\n",
    "    ax.text(x=0, y=outlier_threshold_control+0.01, s=f\"Threshold at {outlier_quantile_threshold*100:.0f}% quantile\")\n",
    "    plt.show()\n",
    "    print(f\"Indices of outliers detected in condition 'control': {outliers_control} (total: {len(outliers_control)})\")\n",
    "\n",
    "    hdbscan_exp = hdbscan.HDBSCAN().fit(tPostSurvey_df.to_numpy().astype(np.float64))\n",
    "    outlier_threshold_exp = pd.Series(hdbscan_exp.outlier_scores_).quantile(outlier_quantile_threshold)\n",
    "    outliers_exp = np.where(hdbscan_exp.outlier_scores_ > outlier_threshold_exp)[0]\n",
    "\n",
    "    tOutlierFilter = hdbscan_exp.outlier_scores_ <= outlier_threshold_exp\n",
    "    tPreSurvey_df = tPreSurvey_df[tOutlierFilter]\n",
    "    tPostSurvey_df = tPostSurvey_df[tOutlierFilter]\n",
    "    tOpinion_df = tOpinion_df[tOutlierFilter]\n",
    "    tOpinion = list(tOpinion_df[tOpinion_df.columns[0]])\n",
    "\n",
    "    sns.set(rc={'figure.figsize':(6,3)})\n",
    "    ax = plt.subplots()\n",
    "    ax = sns.barplot(x=list(range(len(hdbscan_exp.outlier_scores_))), y=hdbscan_exp.outlier_scores_)\n",
    "    ax.set_xlabel(\"Participant (2x2x3 group)\")\n",
    "    ax.set_ylabel(\"GLOSH outlier score\")\n",
    "    ax.axhline(outlier_threshold_exp)\n",
    "    ax.text(x=0, y=outlier_threshold_exp+0.01, s=f\"Threshold at {outlier_quantile_threshold*100:.0f}% quantile\")\n",
    "    plt.show()\n",
    "    print(f\"Indices of outliers detected in condition '2x2x3': {outliers_exp} (total: {len(outliers_exp)})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cPostSurvey_df[\"condition\"] = \"Control\"\n",
    "tPostSurvey_df[\"condition\"] = \"2x2x3\"\n",
    "aPostSurvey_df = cPostSurvey_df.append(tPostSurvey_df).reset_index()\n",
    "aPostSurvey_df = aPostSurvey_df.drop(columns=['index'])\n",
    "aPostSurvey_pivot_df = aPostSurvey_df.melt(id_vars=\"condition\")\n",
    "aPostSurvey_pivot_df = aPostSurvey_pivot_df[aPostSurvey_pivot_df[\"value\"] > 0]\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,4)})\n",
    "ax = plt.subplots()\n",
    "ax = sns.barplot(data=aPostSurvey_pivot_df, x=\"variable\", y=\"value\", hue=\"condition\", errorbar=None)\n",
    "ax.set_xticklabels(list(aPostSurvey_df.columns)[:-1],fontsize=8,rotation=30,ha=\"right\")\n",
    "ax.set_xlabel(\"Question\")\n",
    "plt.show()\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,4)})\n",
    "ax = plt.subplots()\n",
    "ax = sns.boxplot(data=aPostSurvey_pivot_df, x=\"variable\", y=\"value\", hue=\"condition\", showmeans=True)\n",
    "ax.set_xticklabels(list(aPostSurvey_df.columns)[:-1],fontsize=8,rotation=30,ha=\"right\")\n",
    "ax.set_xlabel(\"Question\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup for statistical tests\n",
    "def convert_to_3_point(df, midpoint=3):\n",
    "    df = df.select_dtypes(include=\"int64\")\n",
    "    df_3_point = df.copy()\n",
    "    df_3_point[(df > 0) & (df < midpoint)] = 1\n",
    "    df_3_point[df == midpoint] = 2\n",
    "    df_3_point[df > midpoint] = 3\n",
    "    return df_3_point\n",
    "\n",
    "def f_test_xy(x, y):\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    x_var = np.var(x, ddof=1)\n",
    "    y_var = np.var(y, ddof=1)\n",
    "    if y_var > x_var:\n",
    "        x, y, x_var, y_var = y, x, y_var, x_var\n",
    "    f = x_var/y_var #calculate F test statistic \n",
    "    dfn = x.size-1 #define degrees of freedom numerator \n",
    "    dfd = y.size-1 #define degrees of freedom denominator \n",
    "    p = 1-stats.f.cdf(f, dfn, dfd) #find p-value of F test statistic \n",
    "    return f, p\n",
    "\n",
    "def ftest(exp_df, control_df):\n",
    "    exp_df = exp_df.select_dtypes(include=\"int64\")\n",
    "    control_df = control_df.select_dtypes(include=\"int64\")\n",
    "    ftest_results = []\n",
    "    for column in control_df.columns:\n",
    "        ftest_exp = exp_df[column].to_numpy().astype(np.float64)\n",
    "        ftest_exp = ftest_exp[ftest_exp > 0.]\n",
    "        ftest_control = control_df[column].to_numpy().astype(np.float64)\n",
    "        ftest_control = ftest_control[ftest_control > 0.]\n",
    "        ftest_res = f_test_xy(ftest_exp, ftest_control)\n",
    "        ftest_results.append(ftest_res[1])\n",
    "    ftest_df = pd.DataFrame({\"question\": list(control_df.columns), \"p-value\": ftest_results})\n",
    "    return ftest_df\n",
    "\n",
    "def ttest(exp_df, control_df, ftest_results_df, **ttest_kwargs):\n",
    "    exp_df = exp_df.select_dtypes(include=\"int64\")\n",
    "    control_df = control_df.select_dtypes(include=\"int64\")\n",
    "    # need to do each column separately to ignore 0 (not specified) values.\n",
    "    ttest_results = []\n",
    "    for column in control_df.columns:\n",
    "        ttest_exp = exp_df[column].to_numpy().astype(np.float64)\n",
    "        ttest_exp = ttest_exp[ttest_exp > 0.]\n",
    "        ttest_control = control_df[column].to_numpy().astype(np.float64)\n",
    "        ttest_control = ttest_control[ttest_control > 0.]\n",
    "        equal_var = ftest_results_df[ftest_results_df.question == column][\"p-value\"].item() >= 0.05\n",
    "        ttest_res = ttest_ind(ttest_exp, ttest_control, equal_var=equal_var, **ttest_kwargs)\n",
    "        ttest_results.append(ttest_res.pvalue)\n",
    "    ttest_df = pd.DataFrame({\"question\": list(control_df.columns), \"p-value\": ttest_results})\n",
    "    return ttest_df\n",
    "\n",
    "def chisquare_(exp_df, control_df, **chisquare_kwargs):\n",
    "    exp_df = exp_df.select_dtypes(include=\"int64\")\n",
    "    control_df = control_df.select_dtypes(include=\"int64\")\n",
    "    chisquare_results = []\n",
    "    for column in control_df.columns:\n",
    "        # reindex takes care of filtering out zeros - we only care about the counts of ratings 1-3\n",
    "        cFreq = control_df[column].value_counts().reindex(range(1,4), fill_value=0)\n",
    "        tFreq = exp_df[column].value_counts().reindex(range(1,4), fill_value=0)\n",
    "        chisquare_res = chisquare(tFreq, cFreq, **chisquare_kwargs)\n",
    "        chisquare_results.append(chisquare_res.pvalue)\n",
    "    chisquare_df = pd.DataFrame({\"question\": list(control_df.columns), \"p-value\": chisquare_results})\n",
    "    return chisquare_df\n",
    "    \n",
    "cPostSurvey_df_3_point = convert_to_3_point(cPostSurvey_df)\n",
    "tPostSurvey_df_3_point = convert_to_3_point(tPostSurvey_df)\n",
    "\n",
    "def color_stat_sig(val):\n",
    "    color = \"red\" if val < 0.05 else \"white\"\n",
    "    return f\"color: {color}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_625812/3150259825.py:17: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  f = x_var/y_var #calculate F test statistic\n",
      "/home/sandea5/anaconda3/envs/gator/lib/python3.8/site-packages/scipy/stats/stats.py:6125: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  terms = (f_obs.astype(np.float64) - f_exp)**2 / f_exp\n",
      "/home/sandea5/anaconda3/envs/gator/lib/python3.8/site-packages/scipy/stats/stats.py:6125: RuntimeWarning: invalid value encountered in true_divide\n",
      "  terms = (f_obs.astype(np.float64) - f_exp)**2 / f_exp\n"
     ]
    }
   ],
   "source": [
    "# f-test\n",
    "ftest_results_df = ftest(tPostSurvey_df_3_point, cPostSurvey_df_3_point)\n",
    "\n",
    "# two-tailed t-test (testing if treatment is different from control)\n",
    "ttest_twosided_df = ttest(tPostSurvey_df_3_point, cPostSurvey_df_3_point, ftest_results_df)\n",
    "\n",
    "# one-tailed t-test (testing if treatment is greater than control)\n",
    "ttest_greater_df = ttest(tPostSurvey_df_3_point, cPostSurvey_df_3_point, ftest_results_df, alternative=\"greater\")\n",
    "\n",
    "# one-tailed t-test (testing if treatment is less than control)\n",
    "ttest_less_df = ttest(tPostSurvey_df_3_point, cPostSurvey_df_3_point, ftest_results_df, alternative=\"less\")\n",
    "\n",
    "# Chisquare\n",
    "chisquare_results_df = chisquare_(tPostSurvey_df_3_point, cPostSurvey_df_3_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            color:  red;\n",
       "        }</style><table id=\"T_28300ad2_272c_11ee_b49d_9f8152075392\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >question</th>        <th class=\"col_heading level0 col1\" >p-val (ttest 2-side)</th>        <th class=\"col_heading level0 col2\" >p-val (ttest 1-side-gt)</th>        <th class=\"col_heading level0 col3\" >p-val (ttest 1-side-lt)</th>        <th class=\"col_heading level0 col4\" >p-val (chisquare)</th>        <th class=\"col_heading level0 col5\" >p-val (ftest)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col0\" class=\"data row0 col0\" >donate_in_future</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col1\" class=\"data row0 col1\" >0.587121</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col2\" class=\"data row0 col2\" >0.706440</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col3\" class=\"data row0 col3\" >0.293560</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col4\" class=\"data row0 col4\" >0.716531</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row0_col5\" class=\"data row0 col5\" >0.339920</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col0\" class=\"data row1 col0\" >desire_to_donate</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col1\" class=\"data row1 col1\" >0.811929</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col2\" class=\"data row1 col2\" >0.405964</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col3\" class=\"data row1 col3\" >0.594036</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col4\" class=\"data row1 col4\" >0.704688</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row1_col5\" class=\"data row1 col5\" >0.358485</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col0\" class=\"data row2 col0\" >use_chatbot_again</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col1\" class=\"data row2 col1\" >0.030502</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col2\" class=\"data row2 col2\" >0.984749</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col3\" class=\"data row2 col3\" >0.015251</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row2_col5\" class=\"data row2 col5\" >0.003647</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col0\" class=\"data row3 col0\" >chatbot_is_convincing</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col1\" class=\"data row3 col1\" >0.655946</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col2\" class=\"data row3 col2\" >0.327973</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col3\" class=\"data row3 col3\" >0.672027</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col4\" class=\"data row3 col4\" >0.294575</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row3_col5\" class=\"data row3 col5\" >0.455984</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col0\" class=\"data row4 col0\" >chatbot_pressured_me</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col1\" class=\"data row4 col1\" >0.826443</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col2\" class=\"data row4 col2\" >0.586778</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col3\" class=\"data row4 col3\" >0.413222</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col4\" class=\"data row4 col4\" >0.736714</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row4_col5\" class=\"data row4 col5\" >0.494675</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col0\" class=\"data row5 col0\" >chatbot_was_dishonest</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col1\" class=\"data row5 col1\" >0.245172</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col2\" class=\"data row5 col2\" >0.877414</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col3\" class=\"data row5 col3\" >0.122586</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col4\" class=\"data row5 col4\" >0.393837</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row5_col5\" class=\"data row5 col5\" >0.002687</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col0\" class=\"data row6 col0\" >chatbot_connected_beliefs</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col1\" class=\"data row6 col1\" >0.799290</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col2\" class=\"data row6 col2\" >0.600355</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col3\" class=\"data row6 col3\" >0.399645</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col4\" class=\"data row6 col4\" >0.846482</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row6_col5\" class=\"data row6 col5\" >0.464229</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col0\" class=\"data row7 col0\" >chatbot_is_competent</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col1\" class=\"data row7 col1\" >0.667828</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col2\" class=\"data row7 col2\" >0.666086</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col3\" class=\"data row7 col3\" >0.333914</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col4\" class=\"data row7 col4\" >0.818731</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row7_col5\" class=\"data row7 col5\" >0.479558</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col0\" class=\"data row8 col0\" >chatbot_is_confident</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col1\" class=\"data row8 col1\" >0.100777</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col2\" class=\"data row8 col2\" >0.050388</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col3\" class=\"data row8 col3\" >0.949612</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col4\" class=\"data row8 col4\" >0.135335</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row8_col5\" class=\"data row8 col5\" >0.002116</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col0\" class=\"data row9 col0\" >chatbot_is_efficient</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col1\" class=\"data row9 col1\" >0.167403</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col2\" class=\"data row9 col2\" >0.916299</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col3\" class=\"data row9 col3\" >0.083701</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col4\" class=\"data row9 col4\" >0.099309</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row9_col5\" class=\"data row9 col5\" >0.403530</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col0\" class=\"data row10 col0\" >chatbot_is_intelligent</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col1\" class=\"data row10 col1\" >0.788523</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col2\" class=\"data row10 col2\" >0.394262</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col3\" class=\"data row10 col3\" >0.605738</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col4\" class=\"data row10 col4\" >0.558035</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row10_col5\" class=\"data row10 col5\" >0.307797</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col0\" class=\"data row11 col0\" >chatbot_is_friendly</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col1\" class=\"data row11 col1\" >0.072707</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col2\" class=\"data row11 col2\" >0.036354</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col3\" class=\"data row11 col3\" >0.963646</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col4\" class=\"data row11 col4\" >0.080953</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row11_col5\" class=\"data row11 col5\" >0.001575</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col0\" class=\"data row12 col0\" >chatbot_is_well_intentioned</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col1\" class=\"data row12 col1\" >0.297458</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col2\" class=\"data row12 col2\" >0.851271</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col3\" class=\"data row12 col3\" >0.148729</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col4\" class=\"data row12 col4\" >nan</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row12_col5\" class=\"data row12 col5\" >0.052661</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col0\" class=\"data row13 col0\" >chatbot_is_trustworthy</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col1\" class=\"data row13 col1\" >0.630918</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col2\" class=\"data row13 col2\" >0.684541</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col3\" class=\"data row13 col3\" >0.315459</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col4\" class=\"data row13 col4\" >0.034218</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row13_col5\" class=\"data row13 col5\" >0.162694</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col0\" class=\"data row14 col0\" >chatbot_was_fluent</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col1\" class=\"data row14 col1\" >1.000000</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col2\" class=\"data row14 col2\" >0.500000</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col3\" class=\"data row14 col3\" >0.500000</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col4\" class=\"data row14 col4\" >0.082085</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row14_col5\" class=\"data row14 col5\" >0.256929</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col0\" class=\"data row15 col0\" >chatbot_generation_quality</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col1\" class=\"data row15 col1\" >0.240254</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col2\" class=\"data row15 col2\" >0.120127</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col3\" class=\"data row15 col3\" >0.879873</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col4\" class=\"data row15 col4\" >0.087639</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row15_col5\" class=\"data row15 col5\" >0.358485</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col0\" class=\"data row16 col0\" >chatbot_was_consistent</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col1\" class=\"data row16 col1\" >0.484864</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col2\" class=\"data row16 col2\" >0.757568</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col3\" class=\"data row16 col3\" >0.242432</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col4\" class=\"data row16 col4\" >0.393837</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row16_col5\" class=\"data row16 col5\" >0.318215</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col0\" class=\"data row17 col0\" >chatbot_response_speed</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col1\" class=\"data row17 col1\" >0.002252</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col2\" class=\"data row17 col2\" >0.998874</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col3\" class=\"data row17 col3\" >0.001126</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col4\" class=\"data row17 col4\" >0.000000</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row17_col5\" class=\"data row17 col5\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col0\" class=\"data row18 col0\" >chatbot_provided_valuable_info</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col1\" class=\"data row18 col1\" >0.526820</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col2\" class=\"data row18 col2\" >0.736590</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col3\" class=\"data row18 col3\" >0.263410</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col4\" class=\"data row18 col4\" >0.286505</td>\n",
       "                        <td id=\"T_28300ad2_272c_11ee_b49d_9f8152075392row18_col5\" class=\"data row18 col5\" >0.255530</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4832985fd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_results_concat_df = pd.concat([\n",
    "    ttest_twosided_df, \n",
    "    ttest_greater_df[\"p-value\"], \n",
    "    ttest_less_df[\"p-value\"], \n",
    "    chisquare_results_df[\"p-value\"],\n",
    "    ftest_results_df[\"p-value\"]\n",
    "    ], axis=1)\n",
    "column_list = [\"question\", \"p-val (ttest 2-side)\", \"p-val (ttest 1-side-gt)\", \"p-val (ttest 1-side-lt)\", \"p-val (chisquare)\", \"p-val (ftest)\"]\n",
    "stats_results_concat_df.columns = column_list\n",
    "stats_results_concat_df.style.applymap(color_stat_sig, subset=column_list[1:]).hide_index()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>donate_in_future (control)</th>\n",
       "      <th>donate_in_future (2x2x3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "   donate_in_future (control)  donate_in_future (2x2x3)\n",
       "1                           6                         7\n",
       "2                           6                         6\n",
       "3                           2                         1"
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      "text/plain": [
       "   desire_to_donate (control)  desire_to_donate (2x2x3)\n",
       "1                           7                         7\n",
       "2                           5                         4\n",
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      "text/plain": [
       "   use_again (control)  use_again (2x2x3)\n",
       "1                    7                 10\n",
       "2                    0                  4\n",
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       "      <th>is_convincing (control)</th>\n",
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       "   is_convincing (control)  is_convincing (2x2x3)\n",
       "1                        9                      7\n",
       "2                        2                      4\n",
       "3                        3                      3"
      ]
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      "text/plain": [
       "   pressured_me (control)  pressured_me (2x2x3)\n",
       "1                       9                    10\n",
       "2                       2                     1\n",
       "3                       3                     3"
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       "   was_dishonest (control)  was_dishonest (2x2x3)\n",
       "1                       11                     13\n",
       "2                        2                      1\n",
       "3                        1                      0"
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       "   connected_beliefs (control)  connected_beliefs (2x2x3)\n",
       "1                            6                          7\n",
       "2                            6                          5\n",
       "3                            2                          2"
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       "   provided_valuable_info (control)  provided_valuable_info (2x2x3)\n",
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   "source": [
    "def show_contingency_table(column):\n",
    "    cFreq = cPostSurvey_df_3_point[column].value_counts().reindex(range(1,4), fill_value=0)\n",
    "    tFreq = tPostSurvey_df_3_point[column].value_counts().reindex(range(1,4), fill_value=0)\n",
    "    ctable = pd.concat([cFreq, tFreq], axis=1)\n",
    "    column_display = column.replace(\"chatbot_\", \"\")\n",
    "    ctable.columns = [f\"{column_display} (control)\", f\"{column_display} (2x2x3)\"]\n",
    "    return ctable\n",
    "\n",
    "for column in cPostSurvey_df_3_point.columns:\n",
    "    display(show_contingency_table(column))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scoring sentiment: 100%|██████████| 1/1 [00:00<00:00, 44.63it/s]\n",
      "Scoring sentiment: 100%|██████████| 1/1 [00:00<00:00, 22.00it/s]\n"
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       "<table>\n",
       "<thead>\n",
       "<tr><th>Opinion (control)                                                                                                                                                                                                                                                                                                                                                                                                                                                                  </th><th style=\"text-align: right;\">  sentiment</th><th style=\"text-align: right;\">  use_chatbot_again</th><th style=\"text-align: right;\">  chatbot_is_friendly</th><th style=\"text-align: right;\">  chatbot_is_confident</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>It seemed to repeat the same responses over and over again.                                                                                                                                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\"> -0.534929 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     2</td></tr>\n",
       "<tr><td>It got the basic questions right, but it was unable to answer specifics like donation goals or online payment methods. A couple of the answers seemed somewhat nonsensical.                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\"> -0.676202 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>It&#x27;s transition into pitching me the charity was forced and it often refused to answer my questions, causing me to lose interest in what it was saying.                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\"> -0.866706 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     3</td></tr>\n",
       "<tr><td>I thought it was a bit brief and unusually concise, not like a person with long responses ( I like to write long passages when I DM with people )                                                                                                                                                                                                                                                                                                                                  </td><td style=\"text-align: right;\">  0.0156674</td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     3</td></tr>\n",
       "<tr><td>it was incredibly shallow.                                                                                                                                                                                                                                                                                                                                                                                                                                                         </td><td style=\"text-align: right;\"> -0.957579 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Some of the statements given by the chatbot were incorrect. When asking about which movies the actor Dwayne &quot;The Rock&quot; was in the chatbot responded with a movie that the actor was not in. Also, even when switching to a new topic the chatbot was still responding with answers to the previous topic that were completely unrelated to the question/statement that was givne.                                                                                                  </td><td style=\"text-align: right;\"> -0.575087 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     5</td></tr>\n",
       "<tr><td>It really doesn&#x27;t back down from a &quot;no&quot;                                                                                                                                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\"> -0.100384 </td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     5</td></tr>\n",
       "<tr><td>The chat bot did not work very hard to inform me about the fact that they were seeking donations                                                                                                                                                                                                                                                                                                                                                                                   </td><td style=\"text-align: right;\"> -0.642636 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     2</td></tr>\n",
       "<tr><td>The chatbot worked perfectly and did its job well, but unfortunately that was a cause I wasn&#x27;t interested in                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\"> -0.565722 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Could not provide more information on what the donation process entails, which is essential to the task it wants me to complete. While it was mostly able to understand and answer my questions, the responses were very brief. For example, it said it wants me to donate to Save the Children, but provided no further information about the organisation that would convince me to donate. The chatbot was, however, extremely fast in responding to my questions or statements.</td><td style=\"text-align: right;\"> -0.222386 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    2</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>I think it certainly did its job of understanding what I was saying, but sometimes it wouldn&#x27;t recognize my intent or it would latch onto the first part of my sentence. Pretty lifelike in that regard I suppose. Interesting bot.                                                                                                                                                                                                                                                </td><td style=\"text-align: right;\">  0.528052 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     2</td></tr>\n",
       "<tr><td>It repeated the same responses over and over                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\"> -0.411642 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Respond not well when I answer &quot;no&quot; to &quot;have you donated before&quot;                                                                                                                                                                                                                                                                                                                                                                                                                   </td><td style=\"text-align: right;\"> -0.805519 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Seems to answer the first part of my query of &quot;Thank you, what is your name&quot;, but overall was accurate in answering my questions and queries                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\">  0.801562 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th>Opinion (control)                                                                                                                                                                                                                                                                                                                                                                                                                                                                  </th><th style=\"text-align: right;\">  sentiment</th><th style=\"text-align: right;\">  use_chatbot_again</th><th style=\"text-align: right;\">  chatbot_is_friendly</th><th style=\"text-align: right;\">  chatbot_is_confident</th></tr>\\n</thead>\\n<tbody>\\n<tr><td>It seemed to repeat the same responses over and over again.                                                                                                                                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\"> -0.534929 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     2</td></tr>\\n<tr><td>It got the basic questions right, but it was unable to answer specifics like donation goals or online payment methods. A couple of the answers seemed somewhat nonsensical.                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\"> -0.676202 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>It&#x27;s transition into pitching me the charity was forced and it often refused to answer my questions, causing me to lose interest in what it was saying.                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\"> -0.866706 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     3</td></tr>\\n<tr><td>I thought it was a bit brief and unusually concise, not like a person with long responses ( I like to write long passages when I DM with people )                                                                                                                                                                                                                                                                                                                                  </td><td style=\"text-align: right;\">  0.0156674</td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     3</td></tr>\\n<tr><td>it was incredibly shallow.                                                                                                                                                                                                                                                                                                                                                                                                                                                         </td><td style=\"text-align: right;\"> -0.957579 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Some of the statements given by the chatbot were incorrect. When asking about which movies the actor Dwayne &quot;The Rock&quot; was in the chatbot responded with a movie that the actor was not in. Also, even when switching to a new topic the chatbot was still responding with answers to the previous topic that were completely unrelated to the question/statement that was givne.                                                                                                  </td><td style=\"text-align: right;\"> -0.575087 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     5</td></tr>\\n<tr><td>It really doesn&#x27;t back down from a &quot;no&quot;                                                                                                                                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\"> -0.100384 </td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     5</td></tr>\\n<tr><td>The chat bot did not work very hard to inform me about the fact that they were seeking donations                                                                                                                                                                                                                                                                                                                                                                                   </td><td style=\"text-align: right;\"> -0.642636 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     2</td></tr>\\n<tr><td>The chatbot worked perfectly and did its job well, but unfortunately that was a cause I wasn&#x27;t interested in                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\"> -0.565722 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Could not provide more information on what the donation process entails, which is essential to the task it wants me to complete. While it was mostly able to understand and answer my questions, the responses were very brief. For example, it said it wants me to donate to Save the Children, but provided no further information about the organisation that would convince me to donate. The chatbot was, however, extremely fast in responding to my questions or statements.</td><td style=\"text-align: right;\"> -0.222386 </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    2</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>I think it certainly did its job of understanding what I was saying, but sometimes it wouldn&#x27;t recognize my intent or it would latch onto the first part of my sentence. Pretty lifelike in that regard I suppose. Interesting bot.                                                                                                                                                                                                                                                </td><td style=\"text-align: right;\">  0.528052 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     2</td></tr>\\n<tr><td>It repeated the same responses over and over                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\"> -0.411642 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Respond not well when I answer &quot;no&quot; to &quot;have you donated before&quot;                                                                                                                                                                                                                                                                                                                                                                                                                   </td><td style=\"text-align: right;\"> -0.805519 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Seems to answer the first part of my query of &quot;Thank you, what is your name&quot;, but overall was accurate in answering my questions and queries                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\">  0.801562 </td><td style=\"text-align: right;\">                  4</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     4</td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average control group opinion sentiment: -0.358 (± 0.517)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>Opinion (2x2x3)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              </th><th style=\"text-align: right;\">   sentiment</th><th style=\"text-align: right;\">  use_chatbot_again</th><th style=\"text-align: right;\">  chatbot_is_friendly</th><th style=\"text-align: right;\">  chatbot_is_confident</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Most of the information was superficial, which makes sense for imitating conversation.  I think it tends to repeat itself, and its grammar needs improvement.                                                                                                                                                                                                                                                                                                                                                                                                </td><td style=\"text-align: right;\">-0.610836   </td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>There was some degree of confusion between what the bot said in one message and another                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td style=\"text-align: right;\">-0.426989   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>It gave short responses to what I said but didn&#x27;t have much to contribute to the conversation, and tried to end the exchange with a &quot;have a good one!&quot; only a few turns after we started talking. If it were a person I would have been kind of annoyed.                                                                                                                                                                                                                                                                                                     </td><td style=\"text-align: right;\">-0.803221   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Not sure if this was mentioned, but I was confused about what it was for and had to initiate the conversation, so to me it just started off with stuff about donating to charity (I realized the purpose by the 2nd message). Also some of the statements made no sense like &quot;who am I donating to?&quot; -&gt; &quot;no one&quot; and &quot;you can donate $0 - $2&quot; (I donate $0 and it thanked me). It certainly felt like a chat bot, which didn&#x27;t incentivize me to talk naturally with it much. Also at the end it just kept saying &quot;thanks&quot; and tried to end the conversation.</td><td style=\"text-align: right;\">-0.692021   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     5</td></tr>\n",
       "<tr><td>It may not have worked correctly in my instance but I can&#x27;t be sure.  The chatbot&#x27;s responses seemed only to restate my messages but with added clarification.                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">-0.539538   </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     3</td></tr>\n",
       "<tr><td>The chatbot could have provided more information, or given a reason to donate.  It didn&#x27;t actually bring up anything about why one should donate.                                                                                                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\">-0.553311   </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>-- Some of the chat statements weren&#x27;t entirely relevant to what I was asking\n",
       "-- The chatbot seemed unable to provide specific examples of how the charity helped children -- it gave a general answer when I asked it to elaborate. I would have preferred something that would give me a more concrete idea of how my donation would be beneficial.\n",
       "-- The URL it provided was missing\n",
       "-- &quot;survey at the end of the survey&quot; -- interesting choice of words\n",
       "-- Immediately asking me how much I wanted to donate as soon as it told me the basic info about the charity felt off-putting-- it felt like it was pressuring me to answer how much I wanted to donate before I even knew whether or not I would\n",
       "-- I was impressed by its statement that the charity is legit -- I wasn&#x27;t expecting it to understand what I meant \n",
       "-- Overall, it had a fairly good grasp of the intent of my questions, even if it was a little vague in its responses and couldn&#x27;t grasp the specifics of what I was saying                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              </td><td style=\"text-align: right;\"> 0.169714   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     5</td></tr>\n",
       "<tr><td>The chatbot talked in circles a lot, and its answers to my questions were vaguer than expected.                                                                                                                                                                                                                                                                                                                                                                                                                                                              </td><td style=\"text-align: right;\">-6.43879e-05</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>I think I have the feeling that the chatbot was trying to force the conversation into the direction of charity and donation, and it was awkward sometimes. Moreover, what says by the chatbot was inconsistent among different turns.                                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\">-0.744092   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Annoying                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     </td><td style=\"text-align: right;\">-0.853629   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     5</td></tr>\n",
       "<tr><td>It seemed to value stored context over actually processing the question posed.                                                                                                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">-0.0362746  </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>The chatbot couldn&#x27;t give me detailed information on the charity                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td style=\"text-align: right;\">-0.679218   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     3</td></tr>\n",
       "<tr><td>It doesn&#x27;t change subject when I feel like there is nothing more I want to talk about.                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\">-0.656821   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "<tr><td>Overall, it answered well, but it lacked understanding emotional nuance (though it&#x27;s understandable that it&#x27;s hard to program that type of recognition)                                                                                                                                                                                                                                                                                                                                                                                                      </td><td style=\"text-align: right;\">-0.24846    </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th>Opinion (2x2x3)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              </th><th style=\"text-align: right;\">   sentiment</th><th style=\"text-align: right;\">  use_chatbot_again</th><th style=\"text-align: right;\">  chatbot_is_friendly</th><th style=\"text-align: right;\">  chatbot_is_confident</th></tr>\\n</thead>\\n<tbody>\\n<tr><td>Most of the information was superficial, which makes sense for imitating conversation.  I think it tends to repeat itself, and its grammar needs improvement.                                                                                                                                                                                                                                                                                                                                                                                                </td><td style=\"text-align: right;\">-0.610836   </td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>There was some degree of confusion between what the bot said in one message and another                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td style=\"text-align: right;\">-0.426989   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>It gave short responses to what I said but didn&#x27;t have much to contribute to the conversation, and tried to end the exchange with a &quot;have a good one!&quot; only a few turns after we started talking. If it were a person I would have been kind of annoyed.                                                                                                                                                                                                                                                                                                     </td><td style=\"text-align: right;\">-0.803221   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Not sure if this was mentioned, but I was confused about what it was for and had to initiate the conversation, so to me it just started off with stuff about donating to charity (I realized the purpose by the 2nd message). Also some of the statements made no sense like &quot;who am I donating to?&quot; -&gt; &quot;no one&quot; and &quot;you can donate $0 - $2&quot; (I donate $0 and it thanked me). It certainly felt like a chat bot, which didn&#x27;t incentivize me to talk naturally with it much. Also at the end it just kept saying &quot;thanks&quot; and tried to end the conversation.</td><td style=\"text-align: right;\">-0.692021   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    5</td><td style=\"text-align: right;\">                     5</td></tr>\\n<tr><td>It may not have worked correctly in my instance but I can&#x27;t be sure.  The chatbot&#x27;s responses seemed only to restate my messages but with added clarification.                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">-0.539538   </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     3</td></tr>\\n<tr><td>The chatbot could have provided more information, or given a reason to donate.  It didn&#x27;t actually bring up anything about why one should donate.                                                                                                                                                                                                                                                                                                                                                                                                            </td><td style=\"text-align: right;\">-0.553311   </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>-- Some of the chat statements weren&#x27;t entirely relevant to what I was asking\\n-- The chatbot seemed unable to provide specific examples of how the charity helped children -- it gave a general answer when I asked it to elaborate. I would have preferred something that would give me a more concrete idea of how my donation would be beneficial.\\n-- The URL it provided was missing\\n-- &quot;survey at the end of the survey&quot; -- interesting choice of words\\n-- Immediately asking me how much I wanted to donate as soon as it told me the basic info about the charity felt off-putting-- it felt like it was pressuring me to answer how much I wanted to donate before I even knew whether or not I would\\n-- I was impressed by its statement that the charity is legit -- I wasn&#x27;t expecting it to understand what I meant \\n-- Overall, it had a fairly good grasp of the intent of my questions, even if it was a little vague in its responses and couldn&#x27;t grasp the specifics of what I was saying                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              </td><td style=\"text-align: right;\"> 0.169714   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     5</td></tr>\\n<tr><td>The chatbot talked in circles a lot, and its answers to my questions were vaguer than expected.                                                                                                                                                                                                                                                                                                                                                                                                                                                              </td><td style=\"text-align: right;\">-6.43879e-05</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>I think I have the feeling that the chatbot was trying to force the conversation into the direction of charity and donation, and it was awkward sometimes. Moreover, what says by the chatbot was inconsistent among different turns.                                                                                                                                                                                                                                                                                                                        </td><td style=\"text-align: right;\">-0.744092   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Annoying                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     </td><td style=\"text-align: right;\">-0.853629   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    3</td><td style=\"text-align: right;\">                     5</td></tr>\\n<tr><td>It seemed to value stored context over actually processing the question posed.                                                                                                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">-0.0362746  </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>The chatbot couldn&#x27;t give me detailed information on the charity                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td style=\"text-align: right;\">-0.679218   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     3</td></tr>\\n<tr><td>It doesn&#x27;t change subject when I feel like there is nothing more I want to talk about.                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td style=\"text-align: right;\">-0.656821   </td><td style=\"text-align: right;\">                  2</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n<tr><td>Overall, it answered well, but it lacked understanding emotional nuance (though it&#x27;s understandable that it&#x27;s hard to program that type of recognition)                                                                                                                                                                                                                                                                                                                                                                                                      </td><td style=\"text-align: right;\">-0.24846    </td><td style=\"text-align: right;\">                  3</td><td style=\"text-align: right;\">                    4</td><td style=\"text-align: right;\">                     4</td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average 2x2x3 group opinion sentiment: -0.477 (± 0.324)\n"
     ]
    }
   ],
   "source": [
    "from value_helpers import get_sentiment2\n",
    "\n",
    "cOpinion_df[\"sentiment\"] = get_sentiment2(cOpinion)\n",
    "cOpinion_df[\"use_chatbot_again\"] = cPostSurvey_df[\"use_chatbot_again\"]\n",
    "cOpinion_df[\"chatbot_is_friendly\"] = cPostSurvey_df[\"chatbot_is_friendly\"]\n",
    "cOpinion_df[\"chatbot_is_confident\"] = cPostSurvey_df[\"chatbot_is_confident\"]\n",
    "tOpinion_df[\"sentiment\"] = get_sentiment2(tOpinion)\n",
    "tOpinion_df[\"use_chatbot_again\"] = tPostSurvey_df[\"use_chatbot_again\"]\n",
    "tOpinion_df[\"chatbot_is_friendly\"] = tPostSurvey_df[\"chatbot_is_friendly\"]\n",
    "tOpinion_df[\"chatbot_is_confident\"] = tPostSurvey_df[\"chatbot_is_confident\"]\n",
    "\n",
    "display(tabulate(cOpinion_df, showindex=False, headers=cOpinion_df.columns, tablefmt=\"html\"))\n",
    "print(f\"Average control group opinion sentiment: {cOpinion_df['sentiment'].mean():.3f} (± {cOpinion_df['sentiment'].std():.3f})\")\n",
    "display(tabulate(tOpinion_df, showindex=False, headers=tOpinion_df.columns, tablefmt=\"html\"))\n",
    "print(f\"Average 2x2x3 group opinion sentiment: {tOpinion_df['sentiment'].mean():.3f} (± {tOpinion_df['sentiment'].std():.3f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "opinions = cOpinion + tOpinion\n",
    "opinions = \" \".join(opinions).lower().replace('\\n','').replace('answer','').replace('chatbot','')\n",
    "cOpinion = \" \".join(cOpinion).lower().replace('\\n','').replace('answer','').replace('chatbot','')\n",
    "tOpinion = \" \".join(tOpinion).lower().replace('\\n','').replace('answer','').replace('chatbot','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from wordcloud import WordCloud, ImageColorGenerator\n",
    "\n",
    "def filter_sentiment_below(word_freqs, threshold=-0.6):\n",
    "    words = list(word_freqs)\n",
    "    sentiments = get_sentiment2(words)\n",
    "    word_freqs = {w: word_freqs[w] for w, sent in zip(words, sentiments) if sent < threshold}\n",
    "    return word_freqs\n",
    "\n",
    "def show_wordcloud(opinion):\n",
    "    wordcloud = WordCloud(width=1600,height=800, collocation_threshold=10)\n",
    "    words = wordcloud.process_text(opinion)\n",
    "    words = filter_sentiment_below(words)\n",
    "    wordcloud = wordcloud.generate_from_frequencies(words)\n",
    "    plt.figure(figsize=(12,4))\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.tight_layout(pad=5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scoring sentiment: 100%|██████████| 4/4 [00:00<00:00, 86.55it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_wordcloud(cOpinion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scoring sentiment: 100%|██████████| 5/5 [00:00<00:00, 83.49it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_wordcloud(tOpinion)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('gator')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "1f9238a0e69fda4ba8118d0b1d8875bdc7125c814b7525463a091f1067dfe51b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}