extensionAI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73599af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standart Libs for AI\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "# This libraries below will be used for NLP\n",
    "from textblob import TextBlob\n",
    "import nltk\n",
    "from textblob.classifiers import NaiveBayesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77aa6d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Data Frame\n",
    "train_set = pd.read_csv('./input/train.csv.zip',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "252adc63",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ItemID</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>SentimentText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>is so sad for my APL frie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>I missed the New Moon trail...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>omg its already 7:30 :O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>.. Omgaga. Im sooo  im gunna CRy. I'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>i think mi bf is cheating on me!!!   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>or i just worry too much?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Juuuuuuuuuuuuuuuuussssst Chillin!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunny Again        Work Tomorrow  :-|  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>handed in my uniform today . i miss you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>hmmmm.... i wonder how she my number @-)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ItemID  Sentiment                                      SentimentText\n",
       "0       1          0                       is so sad for my APL frie...\n",
       "1       2          0                     I missed the New Moon trail...\n",
       "2       3          1                            omg its already 7:30 :O\n",
       "3       4          0            .. Omgaga. Im sooo  im gunna CRy. I'...\n",
       "4       5          0           i think mi bf is cheating on me!!!   ...\n",
       "5       6          0                  or i just worry too much?        \n",
       "6       7          1                 Juuuuuuuuuuuuuuuuussssst Chillin!!\n",
       "7       8          0         Sunny Again        Work Tomorrow  :-|  ...\n",
       "8       9          1        handed in my uniform today . i miss you ...\n",
       "9      10          1           hmmmm.... i wonder how she my number @-)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4fe4014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning text \n",
    "def cleanText(text):\n",
    "    text = re.sub('#[A-Za-z0-9]+', '', text) # removes any string with a '#'\n",
    "    text = re.sub('@[A-Za-z0-9_]+', '', text) # removes any string with a '#'\n",
    "    text = re.sub('\\\\n', '', text) # removes the '\\n' string\n",
    "    text = re.sub('https:\\/\\/\\S+', '', text) # removes any hyperlinks\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fc99ae0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ItemID</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>SentimentText</th>\n",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>is so sad for my APL frie...</td>\n",
       "      <td>is so sad for my APL frie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>I missed the New Moon trail...</td>\n",
       "      <td>I missed the New Moon trail...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>omg its already 7:30 :O</td>\n",
       "      <td>omg its already 7:30 :O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>.. Omgaga. Im sooo  im gunna CRy. I'...</td>\n",
       "      <td>.. Omgaga. Im sooo  im gunna CRy. I'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>i think mi bf is cheating on me!!!   ...</td>\n",
       "      <td>i think mi bf is cheating on me!!!   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99984</th>\n",
       "      <td>99996</td>\n",
       "      <td>0</td>\n",
       "      <td>@Cupcake  seems like a repeating problem   hop...</td>\n",
       "      <td>seems like a repeating problem   hope you're...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99985</th>\n",
       "      <td>99997</td>\n",
       "      <td>1</td>\n",
       "      <td>@cupcake__ arrrr we both replied to each other...</td>\n",
       "      <td>arrrr we both replied to each other over diff...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>99998</td>\n",
       "      <td>0</td>\n",
       "      <td>@CuPcAkE_2120 ya i thought so</td>\n",
       "      <td>ya i thought so</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99987</th>\n",
       "      <td>99999</td>\n",
       "      <td>1</td>\n",
       "      <td>@Cupcake_Dollie Yes. Yes. I'm glad you had mor...</td>\n",
       "      <td>Yes. Yes. I'm glad you had more fun with me.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>@cupcake_kayla haha yes you do</td>\n",
       "      <td>haha yes you do</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99989 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ItemID  Sentiment                                      SentimentText  \\\n",
       "0           1          0                       is so sad for my APL frie...   \n",
       "1           2          0                     I missed the New Moon trail...   \n",
       "2           3          1                            omg its already 7:30 :O   \n",
       "3           4          0            .. Omgaga. Im sooo  im gunna CRy. I'...   \n",
       "4           5          0           i think mi bf is cheating on me!!!   ...   \n",
       "...       ...        ...                                                ...   \n",
       "99984   99996          0  @Cupcake  seems like a repeating problem   hop...   \n",
       "99985   99997          1  @cupcake__ arrrr we both replied to each other...   \n",
       "99986   99998          0                     @CuPcAkE_2120 ya i thought so    \n",
       "99987   99999          1  @Cupcake_Dollie Yes. Yes. I'm glad you had mor...   \n",
       "99988  100000          1                    @cupcake_kayla haha yes you do    \n",
       "\n",
       "                                             CleanedText  \n",
       "0                           is so sad for my APL frie...  \n",
       "1                         I missed the New Moon trail...  \n",
       "2                                omg its already 7:30 :O  \n",
       "3                .. Omgaga. Im sooo  im gunna CRy. I'...  \n",
       "4               i think mi bf is cheating on me!!!   ...  \n",
       "...                                                  ...  \n",
       "99984    seems like a repeating problem   hope you're...  \n",
       "99985   arrrr we both replied to each other over diff...  \n",
       "99986                                   ya i thought so   \n",
       "99987      Yes. Yes. I'm glad you had more fun with me.   \n",
       "99988                                   haha yes you do   \n",
       "\n",
       "[99989 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set['CleanedText'] = train_set['SentimentText'].apply(cleanText)\n",
    "train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5e7ef77",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>is so sad for my APL frie...</td>\n",
       "      <td>neg</td>\n",
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       "      <th>1</th>\n",
       "      <td>I missed the New Moon trail...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>omg its already 7:30 :O</td>\n",
       "      <td>pos</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.. Omgaga. Im sooo  im gunna CRy. I'...</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i think mi bf is cheating on me!!!   ...</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99984</th>\n",
       "      <td>seems like a repeating problem   hope you're...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99985</th>\n",
       "      <td>arrrr we both replied to each other over diff...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>ya i thought so</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99987</th>\n",
       "      <td>Yes. Yes. I'm glad you had more fun with me.</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>haha yes you do</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99989 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             CleanedText Sentiment\n",
       "0                           is so sad for my APL frie...       neg\n",
       "1                         I missed the New Moon trail...       neg\n",
       "2                                omg its already 7:30 :O       pos\n",
       "3                .. Omgaga. Im sooo  im gunna CRy. I'...       neg\n",
       "4               i think mi bf is cheating on me!!!   ...       neg\n",
       "...                                                  ...       ...\n",
       "99984    seems like a repeating problem   hope you're...       neg\n",
       "99985   arrrr we both replied to each other over diff...       pos\n",
       "99986                                   ya i thought so        neg\n",
       "99987      Yes. Yes. I'm glad you had more fun with me.        pos\n",
       "99988                                   haha yes you do        pos\n",
       "\n",
       "[99989 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modifiying DF\n",
    "train_set = train_set.drop(['ItemID', 'SentimentText'], axis=1)\n",
    "train_set=train_set.reindex(columns=['CleanedText', 'Sentiment'])\n",
    "train_set.loc[train_set[\"Sentiment\"] == 0, \"Sentiment\"] = \"neg\"\n",
    "train_set.loc[train_set[\"Sentiment\"] == 1, \"Sentiment\"] = \"pos\"\n",
    "train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0597df52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10184</th>\n",
       "      <td>&amp;quot;My Big Redneck Wedding&amp;quot; is killing ...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99947</th>\n",
       "      <td>how come I was the last one you listed on the...</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>11966</th>\n",
       "      <td>$400 to fix my car... trying to figure out whe...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64003</th>\n",
       "      <td>ha! Don't worry I'm marrying one next year. G...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85226</th>\n",
       "      <td>what do I do to bring it down? Its still 102!</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43621</th>\n",
       "      <td>1525.: i already didi try anything...  you ca...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97911</th>\n",
       "      <td>missed you at graduation   drove all that way...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21225</th>\n",
       "      <td>Good way to ruin your perfect body baby.  I l...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5630</th>\n",
       "      <td>my truck is dead. he lives no more.</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29241</th>\n",
       "      <td>when I was lil this cat attacked me...all I ...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             CleanedText Sentiment\n",
       "10184  &quot;My Big Redneck Wedding&quot; is killing ...       neg\n",
       "99947   how come I was the last one you listed on the...       neg\n",
       "11966  $400 to fix my car... trying to figure out whe...       neg\n",
       "64003   ha! Don't worry I'm marrying one next year. G...       pos\n",
       "85226     what do I do to bring it down? Its still 102!        neg\n",
       "...                                                  ...       ...\n",
       "43621   1525.: i already didi try anything...  you ca...       neg\n",
       "97911   missed you at graduation   drove all that way...       neg\n",
       "21225   Good way to ruin your perfect body baby.  I l...       neg\n",
       "5630                my truck is dead. he lives no more.        neg\n",
       "29241    when I was lil this cat attacked me...all I ...       neg\n",
       "\n",
       "[5000 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dividing DF to train our model\n",
    "df = train_set.iloc[np.random.choice(np.arange(len(train_set)), 5000, False)]\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30f584ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data as input list\n",
    "train = list()\n",
    "for index, row in df.iterrows():\n",
    "    train.append((row['CleanedText'], row['Sentiment']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52b1b492",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training AI to learn Sentiments\n",
    "cl = NaiveBayesClassifier(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b68bb2f-67d6-454d-9c43-2c6ad321ff88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neg'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Little test for trained data with complex keywords\n",
    "prob_dist = cl.prob_classify(\"Why in this good wheater i have ugly face\")\n",
    "prob_dist.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06f003c1-e423-457b-aa6c-8da7bc49a667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Data Distrubutin 0 as Negative, 1 as Positive\n",
    "labels = ['Negative', 'Positive']\n",
    "sizes = [df['Sentiment'].value_counts()[0],\n",
    "         df['Sentiment'].value_counts()[1]\n",
    "        ]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "ax1.axis('equal')\n",
    "plt.title('Sentiment Proportion', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b89554b-b2aa-445a-9c74-63048c5c332c",
   "metadata": {},
   "outputs": [
    {
     "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>CleanedText</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35092</th>\n",
       "      <td>Animation?! Slow down man, and let the rest c...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43815</th>\n",
       "      <td>i couldnt move this morning. i was in so much...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73787</th>\n",
       "      <td>its a great park  harriman is nice too</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56998</th>\n",
       "      <td>Ah isn't it just a nightmare!!I've so much to...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56002</th>\n",
       "      <td>notorious? awesome! thanks</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48144</th>\n",
       "      <td>its quite decent... On it right now... Good s...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35703</th>\n",
       "      <td>You're doing a great job!</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20309</th>\n",
       "      <td>@ the diner with my mommy!  hit me up..</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65069</th>\n",
       "      <td>woke up to my chest burning so bad I can't br...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631</th>\n",
       "      <td>thanks for !  Can you reach down so I can giv...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             CleanedText Sentiment\n",
       "35092   Animation?! Slow down man, and let the rest c...       pos\n",
       "43815   i couldnt move this morning. i was in so much...       neg\n",
       "73787            its a great park  harriman is nice too        pos\n",
       "56998   Ah isn't it just a nightmare!!I've so much to...       neg\n",
       "56002                        notorious? awesome! thanks        pos\n",
       "...                                                  ...       ...\n",
       "48144   its quite decent... On it right now... Good s...       pos\n",
       "35703                         You're doing a great job!        pos\n",
       "20309            @ the diner with my mommy!  hit me up..       pos\n",
       "65069   woke up to my chest burning so bad I can't br...       neg\n",
       "7631    thanks for !  Can you reach down so I can giv...       pos\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Data and configuration\n",
    "test_df = train_set.iloc[np.random.choice(np.arange(len(train_set)), 300, False)]\n",
    "\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a522bdd-60d7-4535-be9d-ce5f062ca0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test data as input list\n",
    "tests = list()\n",
    "for index, row in test_df.iterrows():\n",
    "    tests.append((row['CleanedText'], row['Sentiment']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5f4616b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7066666666666667"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see how well our predictions are\n",
    "cl.accuracy(tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cae2235b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "           contains(sad) = True              neg : pos    =     27.8 : 1.0\n",
      "          contains(sick) = True              neg : pos    =     20.7 : 1.0\n",
      "      contains(happened) = True              neg : pos    =     13.7 : 1.0\n",
      "       contains(welcome) = True              pos : neg    =     12.8 : 1.0\n",
      "         contains(sucks) = True              neg : pos    =      9.6 : 1.0\n",
      "          contains(died) = True              neg : pos    =      9.3 : 1.0\n",
      "       contains(forward) = True              pos : neg    =      8.8 : 1.0\n",
      "           contains(omg) = True              neg : pos    =      8.7 : 1.0\n",
      "         contains(thank) = True              pos : neg    =      8.7 : 1.0\n",
      "       contains(amazing) = True              pos : neg    =      7.7 : 1.0\n"
     ]
    }
   ],
   "source": [
    "# Keyword's ratio on sentiment\n",
    "cl.show_informative_features(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e87e8ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}