data_exploratory_analysis.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Exploratory Analysis**\n",
        "1.   Text Length Distribution\n",
        "2.   Word Frequency Analysis\n",
        "3.   Topic Modeling\n",
        "4.   Sentiment Analysis"
      ],
      "metadata": {
        "id": "1GqCq2RLVpaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read data\n",
        "data_path = 'Mental_Health_FAQ.csv'\n",
        "data = pd.read_csv(data_path)\n",
        "\n",
        "print(data.head(3).to_string(index=False))\n",
        "print(\"\\n\" + \"-\" * 50 + \"\\n\")\n",
        "print(\"Number of rows:\", data.shape[0])\n",
        "print(\"Number of columns:\", data.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RapfDGPO6LGY",
        "outputId": "c06b12a7-02e6-4de5-c996-855a9d933450"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Question_ID                                   Questions                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Answers\n",
            "     1590140 What does it mean to have a mental illness? Mental illnesses are health conditions that disrupt a personâ€™s thoughts, emotions, relationships, and daily functioning. They are associated with distress and diminished capacity to engage in the ordinary activities of daily life.\\nMental illnesses fall along a continuum of severity: some are fairly mild and only interfere with some aspects of life, such as certain phobias. On the other end of the spectrum lie serious mental illnesses, which result in major functional impairment and interference with daily life. These include such disorders as major depression, schizophrenia, and bipolar disorder, and may require that the person receives care in a hospital.\\nIt is important to know that mental illnesses are medical conditions that have nothing to do with a personâ€™s character, intelligence, or willpower. Just as diabetes is a disorder of the pancreas, mental illness is a medical condition due to the brainâ€™s biology.\\nSimilarly to how one would treat diabetes with medication and insulin, mental illness is treatable with a combination of medication and social support. These treatments are highly effective, with 70-90 percent of individuals receiving treatment experiencing a reduction in symptoms and an improved quality of life. With the proper treatment, it is very possible for a person with mental illness to be independent and successful.\n",
            "     2110618             Who does mental illness affect?                                                                                                     It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background.\\nAlthough mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children.\\nAdditionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition.\\nParents and caretakers should be aware of this fact, and take notice of changes in their childâ€™s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).\n",
            "     6361820                 What causes mental illness?                                                                                                          It is estimated that mental illness affects 1 in 5 adults in America, and that 1 in 24 adults have a serious mental illness. Mental illness does not discriminate; it can affect anyone, regardless of gender, age, income, social status, ethnicity, religion, sexual orientation, or background. Although mental illness can affect anyone, certain conditions may be more common in different populations. For instance, eating disorders tend to occur more often in females, while disorders such as attention deficit/hyperactivity disorder is more prevalent in children. Additionally, all ages are susceptible, but the young and the old are especially vulnerable. Mental illnesses usually strike individuals in the prime of their lives, with 75 percent of mental health conditions developing by the age of 24. This makes identification and treatment of mental disorders particularly difficult, because the normal personality and behavioral changes of adolescence may mask symptoms of a mental health condition. Parents and caretakers should be aware of this fact, and take notice of changes in their child’s mood, personality, personal habits, and social withdrawal. When these occur in children under 18, they are referred to as serious emotional disturbances (SEDs).\n",
            "\n",
            "--------------------------------------------------\n",
            "\n",
            "Number of rows: 98\n",
            "Number of columns: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text Length Distribution:** Analyze the length of questions and answers in terms of words and characters to understand the data's verbosity."
      ],
      "metadata": {
        "id": "gixXgve45aCz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        },
        "collapsed": true,
        "id": "-Pnqd8CtytzG",
        "outputId": "d7aa2c17-69a7-4cb8-d29a-6c23575aa56d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ],
            "image/png": 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REaEzZ87k2X/btm0aMmSIRo0apb1796pfv37q16+f9u/fX8aRAwAAVBxFzbkAAEDlV+6LRjNmzNDjjz+ukSNH6uabb9acOXNUs2ZNzZ07N8/+77zzjnr37q1nnnlGbdq00SuvvKJbb71V77//fhlHDgAAUHEUNecCAACVn4uzAyjI5cuXtXv3bk2ePNneVq1aNYWHh2v79u15brN9+3ZFR0c7tEVERGjFihX5Pk9mZqYyMzPtj1NTUyVJaWlpNxB93rIuZV6/E1DF1SiFv71cpfU3WJoxA2WpNM9TpfF3knuuNsaU+NhVSXFyrrLMn5Re8kMCKKTSzHFK62+bmFHWSvM85eT8qVwXjc6ePavs7Gz5+/s7tPv7++vgwYN5bpOcnJxn/+Tk5HyfJyYmRlOnTrW0BwUFFSNqADfs9VnOjqDoKmLMQFkrxb+TCxcuyNvbu9TGr+yKk3ORPwFVRUX8bCVmVCal994oTP5UrotGZWXy5MkOs5NycnJ0/vx51atXTzabzYmRlYy0tDQFBQXp+PHj8vLycnY4Ja6y75/EPlYGlX3/JPaxMqiI+2eM0YULFxQYGOjsUKqc0sifKuJ7sKriWFUcHKuKg2NVMVSG41SU/KlcF43q16+v6tWr6/Tp0w7tp0+fVkBAQJ7bBAQEFKm/JLm5ucnNzc2hzcfHp3hBl2NeXl4V9k1dGJV9/yT2sTKo7PsnsY+VQUXbP2YY3bji5FylmT9VtPdgVcaxqjg4VhUHx6piqOjHqbD5U7m+ELarq6tCQkIUGxtrb8vJyVFsbKzCwsLy3CYsLMyhvyStW7cu3/4AAABVXXFyLgAAUPmV65lGkhQdHa3hw4frtttu0x133KG3335bGRkZGjlypCRp2LBhuummmxQTEyNJGjdunLp166bp06fr/vvv15IlS7Rr1y599NFHztwNAACAcu16ORcAAKh6yn3RaNCgQfrll1/00ksvKTk5WbfccovWrFljv1BjUlKSqlX774Spzp07a/HixXrhhRf0/PPPq0WLFlqxYoXatWvnrF1wOjc3N02ZMsUyhbyyqOz7J7GPlUFl3z+JfawMKvv+oWDXy7nKAu/BioNjVXFwrCoOjlXFUNWOk81wj1oAAAAAAABco1xf0wgAAAAAAADOQdEIAAAAAAAAFhSNAAAAAAAAYEHRCAAAAAAAABYUjSqJmJgY3X777fL09JSfn5/69eun+Ph4hz7du3eXzWZzWJ588kknRVx0L7/8siX+1q1b29dfunRJUVFRqlevnmrXrq2BAwfq9OnTToy4aJo0aWLZP5vNpqioKEkV8/ht2bJFffr0UWBgoGw2m1asWOGw3hijl156SQ0aNJCHh4fCw8N16NAhhz7nz5/X0KFD5eXlJR8fH40aNUrp6elluBcFK2gfs7KyNGnSJLVv3161atVSYGCghg0bppMnTzqMkdexf/3118t4T/J2vWM4YsQIS+y9e/d26FORj6GkPP8ubTab3nrrLXuf8nwMC3N+KMznZ1JSku6//37VrFlTfn5+euaZZ3TlypWy3BVUAbNmzVKTJk3k7u6u0NBQ7dy509khVWpl+fmwadMm3XrrrXJzc1Pz5s01f/780t69Suv111+XzWbT+PHj7W0cp/LjP//5jx599FHVq1dPHh4eat++vXbt2mVfX1L57759+9S1a1e5u7srKChIb775ZpnsX2WRnZ2tF198UcHBwfLw8FCzZs30yiuv6Or7hHGs/o9BpRAREWHmzZtn9u/fb+Li4sx9991nGjVqZNLT0+19unXrZh5//HFz6tQp+5KamurEqItmypQppm3btg7x//LLL/b1Tz75pAkKCjKxsbFm165d5s477zSdO3d2YsRFc+bMGYd9W7dunZFkNm7caIypmMdv9erV5n/+53/MsmXLjCSzfPlyh/Wvv/668fb2NitWrDA//PCDeeCBB0xwcLD57bff7H169+5tOnbsaHbs2GG2bt1qmjdvboYMGVLGe5K/gvYxJSXFhIeHmy+++MIcPHjQbN++3dxxxx0mJCTEYYzGjRubadOmORzbq/92nel6x3D48OGmd+/eDrGfP3/eoU9FPobGGId9O3XqlJk7d66x2WwmISHB3qc8H8PCnB+u9/l55coV065dOxMeHm727t1rVq9eberXr28mT57sjF1CJbVkyRLj6upq5s6daw4cOGAef/xx4+PjY06fPu3s0Cqtsvp8OHLkiKlZs6aJjo42//73v817771nqlevbtasWVOm+1sZ7Ny50zRp0sR06NDBjBs3zt7OcSofzp8/bxo3bmxGjBhhvvvuO3PkyBGzdu1ac/jwYXufksh/U1NTjb+/vxk6dKjZv3+/+fzzz42Hh4f58MMPy3R/K7LXXnvN1KtXz6xatcokJiaapUuXmtq1a5t33nnH3odj9TuKRpXUmTNnjCSzefNme1u3bt0cTi4VzZQpU0zHjh3zXJeSkmJq1Khhli5dam/76aefjCSzffv2MoqwZI0bN840a9bM5OTkGGMq/vG79j/jOTk5JiAgwLz11lv2tpSUFOPm5mY+//xzY4wx//73v40k8/3339v7/Otf/zI2m8385z//KbPYCyuvgsO1du7caSSZY8eO2dsaN25sZs6cWbrBlYD8ikZ9+/bNd5vKeAz79u1r7rnnHoe2inIMjbGeHwrz+bl69WpTrVo1k5ycbO8ze/Zs4+XlZTIzM8t2B1Bp3XHHHSYqKsr+ODs72wQGBpqYmBgnRlW1lNbnw7PPPmvatm3r8FyDBg0yERERpb1LlcqFCxdMixYtzLp16xzyQo5T+TFp0iTTpUuXfNeXVP77wQcfmDp16jicgydNmmRatWpV0rtUad1///3msccec2gbMGCAGTp0qDGGY3U1fp5WSaWmpkqS6tat69C+aNEi1a9fX+3atdPkyZN18eJFZ4RXbIcOHVJgYKCaNm2qoUOHKikpSZK0e/duZWVlKTw83N63devWatSokbZv3+6scIvt8uXLWrhwoR577DHZbDZ7e0U/fldLTExUcnKywzHz9vZWaGio/Zht375dPj4+uu222+x9wsPDVa1aNX333XdlHnNJSE1Nlc1mk4+Pj0P766+/rnr16qlTp0566623KtTPfjZt2iQ/Pz+1atVKTz31lM6dO2dfV9mO4enTp/XPf/5To0aNsqyrKMfw2vNDYT4/t2/frvbt28vf39/eJyIiQmlpaTpw4EAZRo/K6vLly9q9e7fD+7BatWoKDw+vkOfxiqq0Ph+2b9/uMEZuH45t0URFRen++++3vJYcp/Ljq6++0m233aaHHnpIfn5+6tSpkz7++GP7+pLKf7dv3667775brq6u9j4RERGKj4/Xr7/+Wtq7WSl07txZsbGx+vnnnyVJP/zwg7755htFRkZK4lhdzcXZAaDk5eTkaPz48brrrrvUrl07e/sjjzyixo0bKzAwUPv27dOkSZMUHx+vZcuWOTHawgsNDdX8+fPVqlUrnTp1SlOnTlXXrl21f/9+JScny9XV1fIfcX9/fyUnJzsn4BuwYsUKpaSkaMSIEfa2in78rpV7XK5OXnIf565LTk6Wn5+fw3oXFxfVrVu3Qh7XS5cuadKkSRoyZIi8vLzs7WPHjtWtt96qunXratu2bZo8ebJOnTqlGTNmODHawundu7cGDBig4OBgJSQk6Pnnn1dkZKS2b9+u6tWrV7pj+Omnn8rT01MDBgxwaK8oxzCv80NhPj+Tk5Pz/FvNXQfcqLNnzyo7OzvP99nBgwedFFXVUpqfD/n1SUtL02+//SYPD4/S2KVKZcmSJdqzZ4++//57yzqOU/lx5MgRzZ49W9HR0Xr++ef1/fffa+zYsXJ1ddXw4cNLLP9NTk5WcHCwZYzcdXXq1CmV/atMnnvuOaWlpal169aqXr26srOz9dprr2no0KGSSu7/KpXhWFE0qoSioqK0f/9+ffPNNw7to0ePtv+7ffv2atCggXr27KmEhAQ1a9asrMMsstyqryR16NBBoaGhaty4sb788stKdxL75JNPFBkZqcDAQHtbRT9+VV1WVpYefvhhGWM0e/Zsh3XR0dH2f3fo0EGurq564oknFBMTIzc3t7IOtUgGDx5s/3f79u3VoUMHNWvWTJs2bVLPnj2dGFnpmDt3roYOHSp3d3eH9opyDPM7PwAAnw/l1/HjxzVu3DitW7fOcv5B+ZKTk6PbbrtNf/3rXyVJnTp10v79+zVnzhwNHz7cydHhal9++aUWLVqkxYsXq23btoqLi9P48eMVGBjIsboGP0+rZMaMGaNVq1Zp48aNatiwYYF9Q0NDJUmHDx8ui9BKnI+Pj1q2bKnDhw8rICBAly9fVkpKikOf06dPKyAgwDkBFtOxY8e0fv16/elPfyqwX0U/frnH5do7e1x9zAICAnTmzBmH9VeuXNH58+cr1HHNLRgdO3ZM69atc5hllJfQ0FBduXJFR48eLZsAS1DTpk1Vv359+/uyshxDSdq6davi4+Ov+7cplc9jmN/5oTCfnwEBAXn+reauA25U/fr1Vb169QLPCSg9pf35kF8fLy+vSvfFX2nYvXu3zpw5o1tvvVUuLi5ycXHR5s2b9e6778rFxUX+/v4cp3KiQYMGuvnmmx3a2rRpY7+kRknlv5yXb9wzzzyj5557ToMHD1b79u31xz/+UU8//bRiYmIkcayuRtGokjDGaMyYMVq+fLk2bNhgmQKXl7i4OEm/f7hVROnp6UpISFCDBg0UEhKiGjVqKDY21r4+Pj5eSUlJCgsLc2KURTdv3jz5+fnp/vvvL7BfRT9+wcHBCggIcDhmaWlp+u677+zHLCwsTCkpKdq9e7e9z4YNG5STk2MvmpV3uQWjQ4cOaf369apXr951t4mLi1O1atUs010rghMnTujcuXP292VlOIa5PvnkE4WEhKhjx47X7VuejuH1zg+F+fwMCwvTjz/+6JAY5RZAr02OgeJwdXVVSEiIw/swJydHsbGxFe48XpGU1edDWFiYwxi5fTi2hdOzZ0/9+OOPiouLsy+33Xabhg4dav83x6l8uOuuuxQfH+/Q9vPPP6tx48aSSi7/DQsL05YtW5SVlWXvs27dOrVq1apC/NypPLh48aKqVXMsh1SvXl05OTmSOFYOnHsdbpSUp556ynh7e5tNmzY53PL54sWLxhhjDh8+bKZNm2Z27dplEhMTzcqVK03Tpk3N3Xff7eTIC2/ChAlm06ZNJjEx0Xz77bcmPDzc1K9f35w5c8YY8/utRhs1amQ2bNhgdu3aZcLCwkxYWJiToy6a7Oxs06hRIzNp0iSH9op6/C5cuGD27t1r9u7daySZGTNmmL1799rvHPb6668bHx8fs3LlSrNv3z7Tt2/fPG9j2alTJ/Pdd9+Zb775xrRo0aJc3a69oH28fPmyeeCBB0zDhg1NXFycw99m7h0Utm3bZmbOnGni4uJMQkKCWbhwofH19TXDhg1z8p79rqD9u3Dhgpk4caLZvn27SUxMNOvXrze33nqradGihbl06ZJ9jIp8DHOlpqaamjVrmtmzZ1u2L+/H8HrnB2Ou//mZe6vmXr16mbi4OLNmzRrj6+vrcKtm4EYtWbLEuLm5mfnz55t///vfZvTo0cbHx8fhbk8oWWX1+ZB7K/dnnnnG/PTTT2bWrFncyv0GXXtXXY5T+bBz507j4uJiXnvtNXPo0CGzaNEiU7NmTbNw4UJ7n5LIf1NSUoy/v7/54x//aPbv32+WLFliatasWaFu4+5sw4cPNzfddJNZtWqVSUxMNMuWLTP169c3zz77rL0Px+p3FI0qCUl5LvPmzTPGGJOUlGTuvvtuU7duXePm5maaN29unnnmGZOamurcwItg0KBBpkGDBsbV1dXcdNNNZtCgQebw4cP29b/99pv585//bOrUqWNq1qxp+vfvb06dOuXEiItu7dq1RpKJj493aK+ox2/jxo15vi+HDx9ujPn9VpYvvvii8ff3N25ubqZnz56WfT937pwZMmSIqV27tvHy8jIjR440Fy5ccMLe5K2gfUxMTMz3b3Pjxo3GGGN2795tQkNDjbe3t3F3dzdt2rQxf/3rXx2KLs5U0P5dvHjR9OrVy/j6+poaNWqYxo0bm8cff9zyH7yKfAxzffjhh8bDw8OkpKRYti/vx/B65wdjCvf5efToURMZGWk8PDxM/fr1zYQJE0xWVlYZ7w0qu/fee880atTIuLq6mjvuuMPs2LHD2SFVamX5+bBx40Zzyy23GFdXV9O0aVOH50DRXVs04jiVH//4xz9Mu3btjJubm2ndurX56KOPHNaXVP77ww8/mC5duhg3Nzdz0003mddff73U960ySUtLM+PGjTONGjUy7u7upmnTpuZ//ud/7F/sGsOxymUzxpgSn74EAAAAAACACo1rGgEAAAAAAMCCohEAAAAAAAAsKBoBAAAAAADAgqIRAAAAAAAALCgaAQAAAAAAwIKiEQAAAAAAACwoGgEAAAAAAMCCohEAp7LZbFqxYoWzwwAAAKgwyJ8AlBWKRgBKVXJysv7yl7+oadOmcnNzU1BQkPr06aPY2Fhnh3ZdI0aMUL9+/ZwdBgAAqGLInwCUFy7ODgBA5XX06FHddddd8vHx0VtvvaX27dsrKytLa9euVVRUlA4ePFgqz3v58mW5urqWytjFUd7iAQAA5Rf50+/KWzxAVcVMIwCl5s9//rNsNpt27typgQMHqmXLlmrbtq2io6O1Y8cOe7+zZ8+qf//+qlmzplq0aKGvvvrKvi47O1ujRo1ScHCwPDw81KpVK73zzjsOz5P7jdZrr72mwMBAtWrVSpL02Wef6bbbbpOnp6cCAgL0yCOP6MyZMw7bHjhwQH/4wx/k5eUlT09Pde3aVQkJCXr55Zf16aefauXKlbLZbLLZbNq0aZMk6fjx43r44Yfl4+OjunXrqm/fvjp69Oh14/nggw/UokULubu7y9/fXw8++GBJvtwAAKASIH8ifwLKE2YaASgV58+f15o1a/Taa6+pVq1alvU+Pj72f0+dOlVvvvmm3nrrLb333nsaOnSojh07prp16yonJ0cNGzbU0qVLVa9ePW3btk2jR49WgwYN9PDDD9vHiI2NlZeXl9atW2dvy8rK0iuvvKJWrVrpzJkzio6O1ogRI7R69WpJ0n/+8x/dfffd6t69uzZs2CAvLy99++23unLliiZOnKiffvpJaWlpmjdvniSpbt26ysrKUkREhMLCwrR161a5uLjo1VdfVe/evbVv3z77N2LXxrNr1y6NHTtWn332mTp37qzz589r69atJf66AwCAiov8ifwJKHcMAJSC7777zkgyy5YtK7CfJPPCCy/YH6enpxtJ5l//+le+20RFRZmBAwfaHw8fPtz4+/ubzMzMAp/r+++/N5LMhQsXjDHGTJ482QQHB5vLly/n2X/48OGmb9++Dm2fffaZadWqlcnJybG3ZWZmGg8PD7N27dp84/n73/9uvLy8TFpaWoExAgCAqov8ifwJKG/4eRqAUmGMKXTfDh062P9dq1YteXl5OUyDnjVrlkJCQuTr66vatWvro48+UlJSksMY7du3t/zufffu3erTp48aNWokT09PdevWTZLs28bFxalr166qUaNGoWP94YcfdPjwYXl6eqp27dqqXbu26tatq0uXLikhISHfeO699141btxYTZs21R//+EctWrRIFy9eLPTzAgCAyo/8ifwJKG8oGgEoFS1atJDNZivUxRqvTTpsNptycnIkSUuWLNHEiRM1atQoff3114qLi9PIkSN1+fJlh22uncKdkZGhiIgIeXl5adGiRfr++++1fPlySbJv6+HhUeT9Sk9PV0hIiOLi4hyWn3/+WY888ki+8Xh6emrPnj36/PPP1aBBA7300kvq2LGjUlJSihwDAAConMifyJ+A8oaiEYBSUbduXUVERGjWrFnKyMiwrC/syf7bb79V586d9ec//1mdOnVS8+bNHb6Rys/Bgwd17tw5vf766+ratatat25tuYhjhw4dtHXrVmVlZeU5hqurq7Kzsx3abr31Vh06dEh+fn5q3ry5w+Lt7V1gTC4uLgoPD9ebb76pffv26ejRo9qwYcN19wUAAFQN5E9W5E+Ac1E0AlBqZs2apezsbN1xxx36+9//rkOHDumnn37Su+++q7CwsEKN0aJFC+3atUtr167Vzz//rBdffFHff//9dbdr1KiRXF1d9d577+nIkSP66quv9Morrzj0GTNmjNLS0jR48GDt2rVLhw4d0meffab4+HhJUpMmTbRv3z7Fx8fr7NmzysrK0tChQ1W/fn317dtXW7duVWJiojZt2qSxY8fqxIkT+cazatUqvfvuu4qLi9OxY8e0YMEC5eTk2O8MAgAAIJE/XY38CXA+ikYASk3Tpk21Z88e9ejRQxMmTFC7du107733KjY2VrNnzy7UGE888YQGDBigQYMGKTQ0VOfOndOf//zn627n6+ur+fPna+nSpbr55pv1+uuv63//938d+tSrV08bNmxQenq6unXrppCQEH388cf26d6PP/64WrVqpdtuu02+vr769ttvVbNmTW3ZskWNGjXSgAED1KZNG40aNUqXLl2Sl5dXvvH4+Pho2bJluueee9SmTRvNmTNHn3/+udq2bVuo1wEAAFQN5E//Rf4EOJ/NFOVqawAAAAAAAKgSmGkEAAAAAAAAC4pGAAAAAAAAsKBoBAAAAAAAAAuKRgAAAAAAALCgaAQAAAAAAAALikYAAAAAAACwoGgEAAAAAAAAC4pGAAAAAAAAsKBoBAAAAAAAAAuKRgAAAAAAALCgaAQAAAAAAAALikYAAAAAAACw+P9C1Dog8ipRVAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate words and characters length\n",
        "data['Question_Word_Count'] = data['Questions'].apply(lambda x: len(x.split()))\n",
        "data['Answer_Word_Count'] = data['Answers'].apply(lambda x: len(x.split()))\n",
        "data['Question_Char_Count'] = data['Questions'].apply(len)\n",
        "data['Answer_Char_Count'] = data['Answers'].apply(len)\n",
        "\n",
        "# Plot\n",
        "fig, ax = plt.subplots(2, 2, figsize=(14, 10))\n",
        "fig.suptitle('Text Length Distribution', fontsize=16)\n",
        "\n",
        "# Question word count distribution\n",
        "ax[0, 0].hist(data['Question_Word_Count'], bins=20, color='skyblue')\n",
        "ax[0, 0].set_title('Question Word Count')\n",
        "ax[0, 0].set_xlabel('Words')\n",
        "ax[0, 0].set_ylabel('Frequency')\n",
        "\n",
        "# Answer word count distribution\n",
        "ax[0, 1].hist(data['Answer_Word_Count'], bins=20, color='lightgreen')\n",
        "ax[0, 1].set_title('Answer Word Count')\n",
        "ax[0, 1].set_xlabel('Words')\n",
        "ax[0, 1].set_ylabel('Frequency')\n",
        "\n",
        "# Question character count distribution\n",
        "ax[1, 0].hist(data['Question_Char_Count'], bins=20, color='salmon')\n",
        "ax[1, 0].set_title('Question Character Count')\n",
        "ax[1, 0].set_xlabel('Characters')\n",
        "ax[1, 0].set_ylabel('Frequency')\n",
        "\n",
        "# Answer character count distribution\n",
        "ax[1, 1].hist(data['Answer_Char_Count'], bins=20, color='gold')\n",
        "ax[1, 1].set_title('Answer Character Count')\n",
        "ax[1, 1].set_xlabel('Characters')\n",
        "ax[1, 1].set_ylabel('Frequency')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Word Frequency Analysis:** Find the most common words questions and answers\n"
      ],
      "metadata": {
        "id": "1sFw5vPN5tc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Join Questions and Answers\n",
        "all_text = ' '.join(data['Questions']) + ' ' + ' '.join(data['Answers'])\n",
        "\n",
        "# Tokenize\n",
        "word_tokens = word_tokenize(all_text)\n",
        "\n",
        "# Remove stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "filtered_words = [w for w in word_tokens if w.lower() not in stop_words and w.isalpha()]\n",
        "\n",
        "# Calculate frequency distribution\n",
        "freq_dist = nltk.FreqDist(filtered_words)\n",
        "\n",
        "# Show most 50 common words\n",
        "for word, frequency in freq_dist.most_common(50):\n",
        "    print(f'{word}: {frequency}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5z0j5WPR503U",
        "outputId": "896d314f-e0f7-4a13-cdd6-759cab8c6859"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "health: 218\n",
            "may: 187\n",
            "mental: 181\n",
            "people: 170\n",
            "help: 159\n",
            "like: 105\n",
            "support: 102\n",
            "find: 99\n",
            "BC: 99\n",
            "time: 81\n",
            "use: 80\n",
            "illness: 77\n",
            "disorder: 77\n",
            "one: 77\n",
            "feel: 75\n",
            "doctor: 74\n",
            "symptoms: 73\n",
            "also: 71\n",
            "treatment: 70\n",
            "different: 70\n",
            "information: 67\n",
            "important: 66\n",
            "problems: 65\n",
            "see: 65\n",
            "Health: 65\n",
            "care: 64\n",
            "Mental: 61\n",
            "need: 59\n",
            "experience: 58\n",
            "family: 58\n",
            "loved: 56\n",
            "talk: 54\n",
            "services: 53\n",
            "someone: 50\n",
            "good: 49\n",
            "depression: 49\n",
            "anxiety: 49\n",
            "work: 49\n",
            "make: 46\n",
            "thoughts: 43\n",
            "social: 42\n",
            "problem: 42\n",
            "might: 42\n",
            "know: 41\n",
            "learn: 41\n",
            "many: 41\n",
            "way: 41\n",
            "lot: 41\n",
            "feelings: 39\n",
            "person: 39\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Topic Modeling:** Use Latent Dirichlet Allocation (LDA) to find the latent topics in answers to understand topics in the text."
      ],
      "metadata": {
        "id": "NRm4FWMZ6h0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim import corpora, models\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "answers = data['Answers']\n",
        "\n",
        "# Tokenize and remove stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "texts = [[word for word in word_tokenize(answer.lower()) if word.isalpha() and word not in stop_words] for answer in answers]\n",
        "\n",
        "# Apply LDA\n",
        "dictionary = corpora.Dictionary(texts)\n",
        "LDA_texts = [dictionary.doc2bow(text) for text in texts]\n",
        "\n",
        "lda_model = models.LdaModel(LDA_texts, num_topics=5, id2word=dictionary, passes=15)\n",
        "\n",
        "# Show\n",
        "topics = lda_model.print_topics(num_words=5)\n",
        "for topic in topics:\n",
        "    print(topic)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flgjOfXA6hlc",
        "outputId": "d1f05c68-f6c6-4dbd-f06c-10d7aab7f121"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, '0.019*\"mental\" + 0.014*\"symptoms\" + 0.014*\"treatment\" + 0.013*\"illness\" + 0.011*\"vaping\"')\n",
            "(1, '0.023*\"may\" + 0.016*\"people\" + 0.011*\"disorder\" + 0.009*\"problems\" + 0.008*\"use\"')\n",
            "(2, '0.013*\"people\" + 0.010*\"time\" + 0.010*\"feel\" + 0.010*\"thinking\" + 0.009*\"anxiety\"')\n",
            "(3, '0.031*\"health\" + 0.025*\"mental\" + 0.013*\"people\" + 0.013*\"support\" + 0.012*\"help\"')\n",
            "(4, '0.015*\"loved\" + 0.015*\"one\" + 0.014*\"like\" + 0.013*\"health\" + 0.012*\"help\"')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sentiment Analysis:**"
      ],
      "metadata": {
        "id": "h5tVw6p56ous"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "answers = data['Answers']\n",
        "polarities = []\n",
        "subjectivities = []\n",
        "\n",
        "# Analyze sentiment:\n",
        "# Providing a polarity score indicating the positivity or negativity of the text and a subjectivity score refers to the expression of personal opinions, emotions, judgments, or beliefs in a text.\n",
        "# Unlike objectivity, which represents factual information\n",
        "for i, answer in enumerate(answers):\n",
        "    sentiment = TextBlob(answer).sentiment\n",
        "    polarities.append(sentiment.polarity)\n",
        "    subjectivities.append(sentiment.subjectivity)\n",
        "    print(f'Answer {i+1}: Polarity = {sentiment.polarity}, Subjectivity = {sentiment.subjectivity}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTUMjGJM68XE",
        "outputId": "346fb8b3-6c86-4f8d-e9ff-7ecbd3695055"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer 1: Polarity = 0.029761904761904764, Subjectivity = 0.38160522273425496\n",
            "Answer 2: Polarity = -0.02192118226600985, Subjectivity = 0.42315270935960586\n",
            "Answer 3: Polarity = -0.02192118226600985, Subjectivity = 0.42315270935960586\n",
            "Answer 4: Polarity = -0.07196969696969696, Subjectivity = 0.41168831168831166\n",
            "Answer 5: Polarity = 0.16433566433566432, Subjectivity = 0.5461538461538461\n",
            "Answer 6: Polarity = 0.13636363636363635, Subjectivity = 0.34545454545454546\n",
            "Answer 7: Polarity = 0.31666666666666665, Subjectivity = 0.35000000000000003\n",
            "Answer 8: Polarity = 0.1888888888888889, Subjectivity = 0.49365079365079356\n",
            "Answer 9: Polarity = 0.12291666666666667, Subjectivity = 0.47194940476190483\n",
            "Answer 10: Polarity = 0.17142857142857143, Subjectivity = 0.37142857142857144\n",
            "Answer 11: Polarity = 0.31666666666666665, Subjectivity = 0.35000000000000003\n",
            "Answer 12: Polarity = 0.1703703703703704, Subjectivity = 0.49867724867724866\n",
            "Answer 13: Polarity = 0.1863095238095238, Subjectivity = 0.27619047619047626\n",
            "Answer 14: Polarity = 0.1875, Subjectivity = 0.46718750000000003\n",
            "Answer 15: Polarity = 0.23245614035087722, Subjectivity = 0.7064327485380119\n",
            "Answer 16: Polarity = 0.1380952380952381, Subjectivity = 0.3142857142857143\n",
            "Answer 17: Polarity = 0.1285714285714286, Subjectivity = 0.4142857142857143\n",
            "Answer 18: Polarity = 0.11666666666666668, Subjectivity = 0.38166666666666665\n",
            "Answer 19: Polarity = 0.22857142857142856, Subjectivity = 0.41190476190476194\n",
            "Answer 20: Polarity = 0.16666666666666666, Subjectivity = 0.26666666666666666\n",
            "Answer 21: Polarity = 0.13636363636363635, Subjectivity = 0.45454545454545453\n",
            "Answer 22: Polarity = 0.17142857142857143, Subjectivity = 0.5589285714285714\n",
            "Answer 23: Polarity = 0.08712121212121211, Subjectivity = 0.5246632996632997\n",
            "Answer 24: Polarity = 0.025, Subjectivity = 0.2\n",
            "Answer 25: Polarity = -0.1, Subjectivity = 0.2\n",
            "Answer 26: Polarity = 0.030952380952380953, Subjectivity = 0.705952380952381\n",
            "Answer 27: Polarity = 0.029166666666666674, Subjectivity = 0.5041666666666667\n",
            "Answer 28: Polarity = 0.4666666666666667, Subjectivity = 0.48333333333333334\n",
            "Answer 29: Polarity = -0.022222222222222227, Subjectivity = 0.12222222222222223\n",
            "Answer 30: Polarity = 0.425, Subjectivity = 0.5\n",
            "Answer 31: Polarity = 0.16875, Subjectivity = 0.75625\n",
            "Answer 32: Polarity = 0.35, Subjectivity = 0.65\n",
            "Answer 33: Polarity = 0.775, Subjectivity = 0.55\n",
            "Answer 34: Polarity = 0.1930316091954023, Subjectivity = 0.5917145593869731\n",
            "Answer 35: Polarity = 0.12084380673090352, Subjectivity = 0.49367930456640124\n",
            "Answer 36: Polarity = 0.14496031746031746, Subjectivity = 0.3714229024943311\n",
            "Answer 37: Polarity = 0.09553790803790808, Subjectivity = 0.4179494304494304\n",
            "Answer 38: Polarity = 0.1580976430976431, Subjectivity = 0.422979797979798\n",
            "Answer 39: Polarity = 0.31705861705861704, Subjectivity = 0.5464266214266214\n",
            "Answer 40: Polarity = 0.06525072150072149, Subjectivity = 0.5920596946912736\n",
            "Answer 41: Polarity = -0.02686206759601258, Subjectivity = 0.5738783005984842\n",
            "Answer 42: Polarity = -0.06683433936955065, Subjectivity = 0.47393721302172015\n",
            "Answer 43: Polarity = 0.17142857142857143, Subjectivity = 0.312293956043956\n",
            "Answer 44: Polarity = 0.055555555555555566, Subjectivity = 0.21132478632478632\n",
            "Answer 45: Polarity = 0.16499999999999998, Subjectivity = 0.30666666666666675\n",
            "Answer 46: Polarity = 0.14125, Subjectivity = 0.2529166666666667\n",
            "Answer 47: Polarity = 0.12962962962962962, Subjectivity = 0.35861823361823375\n",
            "Answer 48: Polarity = -0.030555555555555558, Subjectivity = 0.16121031746031744\n",
            "Answer 49: Polarity = 0.15766806722689075, Subjectivity = 0.4439425770308124\n",
            "Answer 50: Polarity = 0.1982335461149021, Subjectivity = 0.3745359160613398\n",
            "Answer 51: Polarity = 0.14142001409443267, Subjectivity = 0.34257525420316115\n",
            "Answer 52: Polarity = 0.18337789661319076, Subjectivity = 0.40047109752992105\n",
            "Answer 53: Polarity = 0.2140625, Subjectivity = 0.41875\n",
            "Answer 54: Polarity = 0.045741758241758225, Subjectivity = 0.3135989010989011\n",
            "Answer 55: Polarity = 0.0, Subjectivity = 0.375\n",
            "Answer 56: Polarity = 0.10806451612903223, Subjectivity = 0.37553763440860205\n",
            "Answer 57: Polarity = 0.03409090909090909, Subjectivity = 0.12727272727272726\n",
            "Answer 58: Polarity = 0.08585858585858586, Subjectivity = 0.21717171717171718\n",
            "Answer 59: Polarity = 0.15302197802197803, Subjectivity = 0.32643467643467644\n",
            "Answer 60: Polarity = 0.08835978835978837, Subjectivity = 0.31779100529100524\n",
            "Answer 61: Polarity = 0.12781776094276093, Subjectivity = 0.4154325998075998\n",
            "Answer 62: Polarity = 0.11702408702408704, Subjectivity = 0.4217948717948718\n",
            "Answer 63: Polarity = 0.0608068783068783, Subjectivity = 0.5234457671957671\n",
            "Answer 64: Polarity = 0.07784090909090907, Subjectivity = 0.34545454545454546\n",
            "Answer 65: Polarity = 0.19727813852813858, Subjectivity = 0.6003576839826841\n",
            "Answer 66: Polarity = 0.1099099099099099, Subjectivity = 0.601036036036036\n",
            "Answer 67: Polarity = 0.14094202898550723, Subjectivity = 0.3902173913043479\n",
            "Answer 68: Polarity = 0.07678571428571428, Subjectivity = 0.3160714285714286\n",
            "Answer 69: Polarity = 0.24603932584269664, Subjectivity = 0.5873189762796504\n",
            "Answer 70: Polarity = 0.21532775673400656, Subjectivity = 0.5849470918741749\n",
            "Answer 71: Polarity = 0.11989247311827957, Subjectivity = 0.47526881720430103\n",
            "Answer 72: Polarity = 0.10589257233994075, Subjectivity = 0.508030492898914\n",
            "Answer 73: Polarity = 0.0990671641791045, Subjectivity = 0.49776119402985064\n",
            "Answer 74: Polarity = 0.05373472447691196, Subjectivity = 0.4516920882936507\n",
            "Answer 75: Polarity = -0.0008503401360544227, Subjectivity = 0.2998299319727891\n",
            "Answer 76: Polarity = 0.0564474532559639, Subjectivity = 0.49092751220410774\n",
            "Answer 77: Polarity = -0.030000000000000002, Subjectivity = 0.33030158730158726\n",
            "Answer 78: Polarity = -0.033024691358024695, Subjectivity = 0.49509479717813043\n",
            "Answer 79: Polarity = 0.12101503759398496, Subjectivity = 0.42235588972431076\n",
            "Answer 80: Polarity = 0.24, Subjectivity = 0.3\n",
            "Answer 81: Polarity = 0.3066666666666667, Subjectivity = 0.47333333333333333\n",
            "Answer 82: Polarity = 0.1875, Subjectivity = 0.290625\n",
            "Answer 83: Polarity = -0.006200159489633174, Subjectivity = 0.41734449760765546\n",
            "Answer 84: Polarity = 0.01929824561403508, Subjectivity = 0.4890350877192981\n",
            "Answer 85: Polarity = -0.007038095238095241, Subjectivity = 0.47696190476190475\n",
            "Answer 86: Polarity = -0.08076923076923077, Subjectivity = 0.4692307692307693\n",
            "Answer 87: Polarity = 0.13454861111111113, Subjectivity = 0.5045486111111112\n",
            "Answer 88: Polarity = 0.015343915343915342, Subjectivity = 0.5254850088183421\n",
            "Answer 89: Polarity = -0.03603219696969698, Subjectivity = 0.3402935606060606\n",
            "Answer 90: Polarity = 0.14921875, Subjectivity = 0.33958333333333335\n",
            "Answer 91: Polarity = 0.1546875, Subjectivity = 0.2916666666666667\n",
            "Answer 92: Polarity = 0.06260180497468634, Subjectivity = 0.510200124734023\n",
            "Answer 93: Polarity = 0.25027281746031754, Subjectivity = 0.6214533730158731\n",
            "Answer 94: Polarity = 0.10519480519480523, Subjectivity = 0.4616883116883117\n",
            "Answer 95: Polarity = 0.07006172839506172, Subjectivity = 0.44310699588477365\n",
            "Answer 96: Polarity = 0.1708333333333334, Subjectivity = 0.5037698412698413\n",
            "Answer 97: Polarity = 0.14497959183673473, Subjectivity = 0.4260408163265306\n",
            "Answer 98: Polarity = 0.09528985507246376, Subjectivity = 0.4190217391304348\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot polarity scores and subjectivity scores\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Histogram Polarity\n",
        "plt.subplot(1, 2, 1)  # 1 row, 2 columns, 1st subplot\n",
        "plt.hist(polarities, bins=20, color='skyblue')\n",
        "plt.title('Histogram of Polarity Scores')\n",
        "plt.xlabel('Polarity')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Histogram Subjectivity\n",
        "plt.subplot(1, 2, 2)  # 1 row, 2 columns, 2nd subplot\n",
        "plt.hist(subjectivities, bins=20, color='lightgreen')\n",
        "plt.title('Histogram of Subjectivity Scores')\n",
        "plt.xlabel('Subjectivity')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "cRJIuSwJ-vId",
        "outputId": "33fc91ed-f916-4f64-c422-3a4dea6f993b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}