Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "787c4327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "158a2797",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1ce6b973",
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_prep\n",
    "import ngram\n",
    "import linguistic_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "45ec3c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1600\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Ori_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Affinia Chicago is one of the worst hotels I h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I recently stayed at the Affina Chicago hotel ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I stayed at the Affina Chicago for my annivers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>If you are looking for a high end hotel on the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I just returned from a long weekend in Chicago...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Label  Rating                                         Ori_Review\n",
       "0      1       1  Affinia Chicago is one of the worst hotels I h...\n",
       "1      1       1  I recently stayed at the Affina Chicago hotel ...\n",
       "2      1       1  I stayed at the Affina Chicago for my annivers...\n",
       "3      1       1  If you are looking for a high end hotel on the...\n",
       "4      1       1  I just returned from a long weekend in Chicago..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_prep.import_chi_review()\n",
    "print(len(df))\n",
    "display(df[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6338d1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Ori_Review</th>\n",
       "      <th>Clean_Review</th>\n",
       "      <th>PP_Review</th>\n",
       "      <th>Word_List</th>\n",
       "      <th>Word_List_all</th>\n",
       "      <th>num_word</th>\n",
       "      <th>num_coreword</th>\n",
       "      <th>num_stopword</th>\n",
       "      <th>num_char</th>\n",
       "      <th>char_per_word</th>\n",
       "      <th>num_first_sing</th>\n",
       "      <th>num_first_pru</th>\n",
       "      <th>num_third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Affinia Chicago is one of the worst hotels I h...</td>\n",
       "      <td>affinia chicago be one of the bad hotel i have...</td>\n",
       "      <td>affinia one bad ever life treat poorly guest f...</td>\n",
       "      <td>[affinia, one, bad, ever, life, treat, poorly,...</td>\n",
       "      <td>[affinia, chicago, be, one, of, the, bad, hote...</td>\n",
       "      <td>166</td>\n",
       "      <td>56</td>\n",
       "      <td>110</td>\n",
       "      <td>845</td>\n",
       "      <td>5.090361</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I recently stayed at the Affina Chicago hotel ...</td>\n",
       "      <td>i recently stay at the affina chicago hotel an...</td>\n",
       "      <td>recently affina really dissappointed arrive de...</td>\n",
       "      <td>[recently, affina, really, dissappointed, arri...</td>\n",
       "      <td>[i, recently, stay, at, the, affina, chicago, ...</td>\n",
       "      <td>224</td>\n",
       "      <td>94</td>\n",
       "      <td>130</td>\n",
       "      <td>1168</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I stayed at the Affina Chicago for my annivers...</td>\n",
       "      <td>i stay at the affina chicago for my anniversar...</td>\n",
       "      <td>affina anniversary great location make poor cu...</td>\n",
       "      <td>[affina, anniversary, great, location, make, p...</td>\n",
       "      <td>[i, stay, at, the, affina, chicago, for, my, a...</td>\n",
       "      <td>69</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>373</td>\n",
       "      <td>5.405797</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>If you are looking for a high end hotel on the...</td>\n",
       "      <td>if you be look for a high end hotel on the mag...</td>\n",
       "      <td>look high end magnificent mile affinia best op...</td>\n",
       "      <td>[look, high, end, magnificent, mile, affinia, ...</td>\n",
       "      <td>[if, you, be, look, for, a, high, end, hotel, ...</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>91</td>\n",
       "      <td>768</td>\n",
       "      <td>5.260274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>I just returned from a long weekend in Chicago...</td>\n",
       "      <td>i just return from a long weekend in chicago w...</td>\n",
       "      <td>return long weekend wife affinia cannot even b...</td>\n",
       "      <td>[return, long, weekend, wife, affinia, cannot,...</td>\n",
       "      <td>[i, just, return, from, a, long, weekend, in, ...</td>\n",
       "      <td>253</td>\n",
       "      <td>101</td>\n",
       "      <td>152</td>\n",
       "      <td>1278</td>\n",
       "      <td>5.051383</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Label  Rating                                         Ori_Review  \\\n",
       "0      1       1  Affinia Chicago is one of the worst hotels I h...   \n",
       "1      1       1  I recently stayed at the Affina Chicago hotel ...   \n",
       "2      1       1  I stayed at the Affina Chicago for my annivers...   \n",
       "3      1       1  If you are looking for a high end hotel on the...   \n",
       "4      1       1  I just returned from a long weekend in Chicago...   \n",
       "\n",
       "                                        Clean_Review  \\\n",
       "0  affinia chicago be one of the bad hotel i have...   \n",
       "1  i recently stay at the affina chicago hotel an...   \n",
       "2  i stay at the affina chicago for my anniversar...   \n",
       "3  if you be look for a high end hotel on the mag...   \n",
       "4  i just return from a long weekend in chicago w...   \n",
       "\n",
       "                                           PP_Review  \\\n",
       "0  affinia one bad ever life treat poorly guest f...   \n",
       "1  recently affina really dissappointed arrive de...   \n",
       "2  affina anniversary great location make poor cu...   \n",
       "3  look high end magnificent mile affinia best op...   \n",
       "4  return long weekend wife affinia cannot even b...   \n",
       "\n",
       "                                           Word_List  \\\n",
       "0  [affinia, one, bad, ever, life, treat, poorly,...   \n",
       "1  [recently, affina, really, dissappointed, arri...   \n",
       "2  [affina, anniversary, great, location, make, p...   \n",
       "3  [look, high, end, magnificent, mile, affinia, ...   \n",
       "4  [return, long, weekend, wife, affinia, cannot,...   \n",
       "\n",
       "                                       Word_List_all  num_word  num_coreword  \\\n",
       "0  [affinia, chicago, be, one, of, the, bad, hote...       166            56   \n",
       "1  [i, recently, stay, at, the, affina, chicago, ...       224            94   \n",
       "2  [i, stay, at, the, affina, chicago, for, my, a...        69            29   \n",
       "3  [if, you, be, look, for, a, high, end, hotel, ...       146            55   \n",
       "4  [i, just, return, from, a, long, weekend, in, ...       253           101   \n",
       "\n",
       "   num_stopword  num_char  char_per_word  num_first_sing  num_first_pru  \\\n",
       "0           110       845       5.090361              15              0   \n",
       "1           130      1168       5.214286              19              0   \n",
       "2            40       373       5.405797               4              0   \n",
       "3            91       768       5.260274               0              0   \n",
       "4           152      1278       5.051383               9             15   \n",
       "\n",
       "   num_third  \n",
       "0          2  \n",
       "1          1  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data_prep.preprocess_ngram(df)\n",
    "df = data_prep.preprocess_ling_feature(df)\n",
    "df = linguistic_feature.ling_feature(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86acb86",
   "metadata": {},
   "source": [
    "## Sample Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8b3e84",
   "metadata": {},
   "source": [
    "#### Deceptive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36ce8989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Terrible experience, I will not stay here again. The walls were so thin that I was kept up all night by the party going on in the suite next to mine. I talked to the management several times, but nothing was done. It may just be the people that I had contact with, but I thought the staff was standoffish and rude. Very unpleasant experience, especially given the cost of staying here!\n",
      "\n",
      "I went to the Homewood Suites in Chicago which is part of Hilton's famous hotels. I gotta say that this is the worst hotel that I have ever been to. In fact, Homewood Suites is the worst hotel on the face of this planet. I checked out the rooms and their rooms look like the hotel was built in bad shape. In fact, the rooms looked so bad that I wanted to leave the hotel early. I also got to check out the employees who worked for this hotel. I saw one of the employees and when I rung the bell for my room, they ignored me. How could a hotel have the worst service when they are supposed to not ignore you? One more thing that I hate about this hotel is that the logo didn't look like the actual logo. It looked more like the logo was changed to look more like something a baby could draw. I'll never go to this hotel again and it won't even be in a million years for that matter. This is a hotel that I will never recommend to anyone because of everything the hotel has to offer.\n",
      "\n",
      "Fantastic stay! The hotel was beautifully decorated and immaculately clean. I chose this place because a friend recommended it plus it was VERY close to my business conference. Here you get the resources and exemplary four star luxury treatment with the downtown Chicago charm. Be sure to try the ribs at South Water Kitchen next door. They are the best I've ever had. EVER! If youre in the mood to unwind, you can sample the evening wine tastings downstairs or take a walk and explore the nightlife all around. I had a great time here. From now on, I won't stay anyplace else.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for txt in df[df['Label'] == 1]['Ori_Review'].sample(3):\n",
    "    print(txt)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c977dba",
   "metadata": {},
   "source": [
    "#### Truthful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "97fd6180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We connected to Affinia Chicago thorugh a city websight. The deal was fair for a downtown hotel on the main drag with an attractiion included. There was information out there that the hotel was being overhauled although we did not pay much attention to that. They are currently under major construction. They will be a little late of their projections in the finish date. I must say that the staff tried very hard to make our stay comfortable under very difficult circumstances. The room was complete and was good. It had a very nice bathroom. The main lobbie and all hotel amenities are totally unfinished drywall with no food, drink, or rest room opportunities. The construction guys did their best to limit activity and noise to after 9:00am. I think this will be a good one once they are done. As of the end of May, they have a way yet to go.\n",
      "\n",
      "After reading all the lukewarm reviews for this hotel, I went ahead and got an Ambassador membership just to hedge off any unpleasantness. I normally have a good experience with Intercontinental hotels, particularly the Mark Hopkins in SF. I called the hotel directly to confirm that my reservation was designated as Ambassador Club. When I arrived at the hotel, there was no separate check-in line (as advertised) and the manager on duty basically ignored me when I told him I was an ambassador club member and stuck me with a regular tiny room. I was nearly half an hour checking in the room and had to deal with the arrogant attitude of the manager on duty. They gave me a room in the \"modern\" tower, (built in the 60's) which was about 2/3 the size of a regular room at the Mark Hopkins (built in '29). I had to deal with torn sheets, cracked marble sinks, and the spare roll of toilet paper being located OUTSIDE the bathroom inside the closet - how convenient is that? The closet door, if you are not careful, may break the full length mirror attached to the bathroom door. I was not provided with a key to the minibar and I called for turndown service, which never happened. The next day, they finally got around to the fact that I was an Ambassador Club member and entitled to an upgraded room, but since I travel with a medical device which I already set up in the tiny room, I declined. To their credit, they did send some of the amenities I would otherwise be entitled to as an Ambassador Club member. I would not stay at this hotel next time I go to Chicago. There are other better places for the money including, the Omni, The James, The Fairmont, Hyatt, Sheraton, Monaco, Crowne Plaza, even the Holiday Inn and Marriotts located in the same general area.\n",
      "\n",
      "I stayed for four nights while attending a conference. The hotel is in a great spot - easy walk to Michigan Ave shopping or Rush St., but just off the busy streets. The room I had was spacious, and very well-appointed. The staff was friendly, and the fitness center, while not huge, was well-equipped and clean. I've stayed at a number of hotels in Chicago, and this one is my favorite. Internet wasn't free, but at $10 for 24 hours is cheaper than most business hotels, and it worked very well.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for txt in df[df['Label'] == -1]['Ori_Review'].sample(3):\n",
    "    print(txt)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd556d5d",
   "metadata": {},
   "source": [
    "## Explore Data Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0956d4",
   "metadata": {},
   "source": [
    "#### Review length distribution (by charactors and words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "59e2ce2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "num_char_dec = df[df['Label'] == 1]['num_char']\n",
    "num_char_tru = df[df['Label'] == -1]['num_char']\n",
    "num_word_dec = df[df['Label'] == 1]['num_char']\n",
    "num_word_tru = df[df['Label'] == -1]['num_char']\n",
    "\n",
    "sns.histplot(num_char_dec, ax=ax1, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_char_tru, ax=ax1, element=\"step\", fill=False, label='Truthful')\n",
    "ax1.axvline(x=num_char_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax1.axvline(x=num_char_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax1.set_title('Sentence Length (Charactor) Histogram')\n",
    "ax1.legend()\n",
    "\n",
    "sns.histplot(num_word_dec, ax=ax2, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_word_tru, ax=ax2, element=\"step\", fill=False, label='Truthful')\n",
    "ax2.axvline(x=num_word_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax2.axvline(x=num_word_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax2.set_title('Sentence Length (Word) Histogram')\n",
    "ax2.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534b98e1",
   "metadata": {},
   "source": [
    "#### Core word and stop word distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "677f64a3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "num_core_dec = df[df['Label'] == 1]['num_coreword']\n",
    "num_core_tru = df[df['Label'] == -1]['num_coreword']\n",
    "num_stop_dec = df[df['Label'] == 1]['num_stopword']\n",
    "num_stop_tru = df[df['Label'] == -1]['num_stopword']\n",
    "\n",
    "sns.histplot(num_core_dec, ax=ax1, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_core_tru, ax=ax1, element=\"step\", fill=False, label='Truthful')\n",
    "ax1.axvline(x=num_core_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax1.axvline(x=num_core_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax1.set_title('Number of Core Words Histogram')\n",
    "ax1.legend()\n",
    "\n",
    "sns.histplot(num_stop_dec, ax=ax2, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_stop_tru, ax=ax2, element=\"step\", fill=False, label='Truthful')\n",
    "ax2.axvline(x=num_stop_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax2.axvline(x=num_stop_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax2.set_title('Number of Stop Words Histogram')\n",
    "ax2.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60403fa4",
   "metadata": {},
   "source": [
    "#### Pronoun Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4bd6ad6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "num_fs_dec = df[df['Label'] == 1]['num_first_sing']\n",
    "num_fs_tru = df[df['Label'] == -1]['num_first_sing']\n",
    "num_fp_dec = df[df['Label'] == 1]['num_first_pru']\n",
    "num_fp_tru = df[df['Label'] == -1]['num_first_pru']\n",
    "num_third_dec = df[df['Label'] == 1]['num_third']\n",
    "num_third_tru = df[df['Label'] == -1]['num_third']\n",
    "\n",
    "sns.histplot(num_f_dec, ax=ax1, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_f_tru, ax=ax1, element=\"step\", fill=False, label='Truthful')\n",
    "ax1.axvline(x=num_f_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax1.axvline(x=num_f_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax1.set_title('First-Person Singular Pronoun Histogram')\n",
    "ax1.set(xlabel='')\n",
    "ax1.legend()\n",
    "\n",
    "sns.histplot(num_fs_dec, ax=ax2, element=\"step\", fill=False, label='Deceptive')\n",
    "sns.histplot(num_fs_tru, ax=ax2, element=\"step\", fill=False, label='Truthful')\n",
    "ax2.axvline(x=num_fs_dec.mean(), label='Avg (Deceptive)', c='g')\n",
    "ax2.axvline(x=num_fs_tru.mean(), label='Avg (Truthful)', c='r')\n",
    "ax2.set_title('First-Person Plural Pronoun Histogram')\n",
    "ax2.set(xlabel='')\n",
    "ax2.legend()\n",
    "\n",
    "plt.savefig('first_person_pronoun.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (base)",
   "language": "python",
   "name": "base"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}