tf-idf-twi-sentiment-analysis.ipynb

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     "text": [
      "['twitter-entity-sentiment-analysis']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import randint\n",
    "import seaborn as sns # used for plot interactive graph. \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from io import StringIO\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_selection import chi2\n",
    "from IPython.display import display\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "plt.rcParams['figure.figsize'] = (14, 8)\n",
    "plt.rcParams['legend.fontsize'] = 14\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "from tqdm.notebook import tqdm as tn\n",
    "\n",
    "import random"
   ]
  },
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Entity</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>is_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
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       "      <td>True</td>\n",
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       "      <td>2401</td>\n",
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       "      <td>True</td>\n",
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       "      <th>3</th>\n",
       "      <td>2401</td>\n",
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       "      <td>Positive</td>\n",
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       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>4891</td>\n",
       "      <td>GrandTheftAuto(GTA)</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>⭐️ Toronto is the arts and culture capital of ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>4359</td>\n",
       "      <td>CS-GO</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>tHIS IS ACTUALLY A GOOD MOVE TOT BRING MORE VI...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2652</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Today sucked so it’s time to drink wine n play...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8069</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Bought a fraction of Microsoft today. Small wins.</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>6960</td>\n",
       "      <td>johnson&amp;johnson</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Johnson &amp; Johnson to stop selling talc baby po...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75682 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id               Entity   Sentiment  \\\n",
       "0    2401          Borderlands    Positive   \n",
       "1    2401          Borderlands    Positive   \n",
       "2    2401          Borderlands    Positive   \n",
       "3    2401          Borderlands    Positive   \n",
       "4    2401          Borderlands    Positive   \n",
       "..    ...                  ...         ...   \n",
       "995  4891  GrandTheftAuto(GTA)  Irrelevant   \n",
       "996  4359                CS-GO  Irrelevant   \n",
       "997  2652          Borderlands    Positive   \n",
       "998  8069            Microsoft    Positive   \n",
       "999  6960      johnson&johnson     Neutral   \n",
       "\n",
       "                                                 Tweet  is_train  \n",
       "0    im getting on borderlands and i will murder yo...      True  \n",
       "1    I am coming to the borders and I will kill you...      True  \n",
       "2    im getting on borderlands and i will kill you ...      True  \n",
       "3    im coming on borderlands and i will murder you...      True  \n",
       "4    im getting on borderlands 2 and i will murder ...      True  \n",
       "..                                                 ...       ...  \n",
       "995  ⭐️ Toronto is the arts and culture capital of ...     False  \n",
       "996  tHIS IS ACTUALLY A GOOD MOVE TOT BRING MORE VI...     False  \n",
       "997  Today sucked so it’s time to drink wine n play...     False  \n",
       "998  Bought a fraction of Microsoft today. Small wins.     False  \n",
       "999  Johnson & Johnson to stop selling talc baby po...     False  \n",
       "\n",
       "[75682 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path = '/kaggle/input/twitter-entity-sentiment-analysis/twitter_training.csv'\n",
    "valid_path = '/kaggle/input/twitter-entity-sentiment-analysis/twitter_validation.csv'\n",
    "cols = ['Id', 'Entity', 'Sentiment', 'Tweet']\n",
    "train = pd.read_csv(train_path, names = cols, header=None)\n",
    "valid = pd.read_csv(valid_path, names = cols)\n",
    "train['is_train'], valid['is_train'] = True, False\n",
    "all_df = train.append(valid)\n",
    "all_df"
   ]
  },
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       "      <td>CS-GO</td>\n",
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       "      <th>75680</th>\n",
       "      <td>8069</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Bought a fraction of Microsoft today. Small wins.</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75681</th>\n",
       "      <td>6960</td>\n",
       "      <td>johnson&amp;johnson</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Johnson &amp; Johnson to stop selling talc baby po...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "<p>75682 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "         Id               Entity   Sentiment  \\\n",
       "0      2401          Borderlands    Positive   \n",
       "1      2401          Borderlands    Positive   \n",
       "2      2401          Borderlands    Positive   \n",
       "3      2401          Borderlands    Positive   \n",
       "4      2401          Borderlands    Positive   \n",
       "...     ...                  ...         ...   \n",
       "75677  4891  GrandTheftAuto(GTA)  Irrelevant   \n",
       "75678  4359                CS-GO  Irrelevant   \n",
       "75679  2652          Borderlands    Positive   \n",
       "75680  8069            Microsoft    Positive   \n",
       "75681  6960      johnson&johnson     Neutral   \n",
       "\n",
       "                                                   Tweet  is_train  \n",
       "0      im getting on borderlands and i will murder yo...      True  \n",
       "1      I am coming to the borders and I will kill you...      True  \n",
       "2      im getting on borderlands and i will kill you ...      True  \n",
       "3      im coming on borderlands and i will murder you...      True  \n",
       "4      im getting on borderlands 2 and i will murder ...      True  \n",
       "...                                                  ...       ...  \n",
       "75677  ⭐️ Toronto is the arts and culture capital of ...     False  \n",
       "75678  tHIS IS ACTUALLY A GOOD MOVE TOT BRING MORE VI...     False  \n",
       "75679  Today sucked so it’s time to drink wine n play...     False  \n",
       "75680  Bought a fraction of Microsoft today. Small wins.     False  \n",
       "75681  Johnson & Johnson to stop selling talc baby po...     False  \n",
       "\n",
       "[75682 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.index = pd.RangeIndex(len(all_df.index))\n",
    "all_df.index = range(len(all_df.index))\n",
    "all_df"
   ]
  },
  {
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   "source": [
    "## EDA"
   ]
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       "                                                           0  \\\n",
       "Id                                                      2401   \n",
       "Entity                                           Borderlands   \n",
       "Sentiment                                           Positive   \n",
       "Tweet      im getting on borderlands and i will murder yo...   \n",
       "is_train                                                True   \n",
       "\n",
       "                                                           1  \n",
       "Id                                                      2401  \n",
       "Entity                                           Borderlands  \n",
       "Sentiment                                           Positive  \n",
       "Tweet      I am coming to the borders and I will kill you...  \n",
       "is_train                                                True  "
      ]
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     "metadata": {},
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    "all_df.head(2).T"
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       "(73996, 2)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_df = train[['Sentiment', 'Tweet']].copy()\n",
    "\n",
    "labeled_df  = labeled_df[pd.notnull(labeled_df['Tweet'])]\n",
    "\n",
    "labeled_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de838b78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:57.696204Z",
     "iopub.status.busy": "2022-02-20T21:04:57.695034Z",
     "iopub.status.idle": "2022-02-20T21:04:57.701851Z",
     "shell.execute_reply": "2022-02-20T21:04:57.702321Z",
     "shell.execute_reply.started": "2022-02-19T20:31:52.922264Z"
    },
    "papermill": {
     "duration": 0.073091,
     "end_time": "2022-02-20T21:04:57.702510",
     "exception": false,
     "start_time": "2022-02-20T21:04:57.629419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment    0\n",
       "Tweet        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "034aed85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:57.795031Z",
     "iopub.status.busy": "2022-02-20T21:04:57.794009Z",
     "iopub.status.idle": "2022-02-20T21:04:57.810213Z",
     "shell.execute_reply": "2022-02-20T21:04:57.810906Z",
     "shell.execute_reply.started": "2022-02-19T20:31:52.939537Z"
    },
    "papermill": {
     "duration": 0.064559,
     "end_time": "2022-02-20T21:04:57.811103",
     "exception": false,
     "start_time": "2022-02-20T21:04:57.746544",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train['Tweet'].notnull().sum()\n",
    "round((total/len(all_df)*100),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3d5e36a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:57.915634Z",
     "iopub.status.busy": "2022-02-20T21:04:57.902724Z",
     "iopub.status.idle": "2022-02-20T21:04:57.920642Z",
     "shell.execute_reply": "2022-02-20T21:04:57.921179Z",
     "shell.execute_reply.started": "2022-02-19T20:31:52.957649Z"
    },
    "papermill": {
     "duration": 0.06532,
     "end_time": "2022-02-20T21:04:57.921352",
     "exception": false,
     "start_time": "2022-02-20T21:04:57.856032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Negative      22358\n",
       "Positive      20655\n",
       "Neutral       18108\n",
       "Irrelevant    12875\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.DataFrame(short_df.Sentiment.unique()).values\n",
    "labeled_df.Sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bcc273d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.018487Z",
     "iopub.status.busy": "2022-02-20T21:04:58.014746Z",
     "iopub.status.idle": "2022-02-20T21:04:58.045058Z",
     "shell.execute_reply": "2022-02-20T21:04:58.045570Z",
     "shell.execute_reply.started": "2022-02-19T20:31:52.973821Z"
    },
    "papermill": {
     "duration": 0.079078,
     "end_time": "2022-02-20T21:04:58.045770",
     "exception": false,
     "start_time": "2022-02-20T21:04:57.966692",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sentiment</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentiment_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56570</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Are we just going to pretend to be @ Ubisoft a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54194</th>\n",
       "      <td>Negative</td>\n",
       "      <td>The problem is even more than racism, it's a d...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64054</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Hey @EAMaddenNFL but when else is the update c...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42756</th>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>Can Mingyu also play PUBG huhuhu I want him to...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38295</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@BlizzardCSEU_EN Any logical reason why my hea...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18029</th>\n",
       "      <td>Positive</td>\n",
       "      <td>U could hear that ps1 was in a joint.. dope......</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29309</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@EA @PlayApex I’ve been trying to play the gam...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29737</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>I really had a bad feeling to clear ppl with t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45666</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>Eh.   Probably for the best.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48357</th>\n",
       "      <td>Positive</td>\n",
       "      <td>When the State tells you you safe to go to Hom...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment                                              Tweet  \\\n",
       "56570    Positive  Are we just going to pretend to be @ Ubisoft a...   \n",
       "54194    Negative  The problem is even more than racism, it's a d...   \n",
       "64054    Negative  Hey @EAMaddenNFL but when else is the update c...   \n",
       "42756  Irrelevant  Can Mingyu also play PUBG huhuhu I want him to...   \n",
       "38295    Negative  @BlizzardCSEU_EN Any logical reason why my hea...   \n",
       "...           ...                                                ...   \n",
       "18029    Positive  U could hear that ps1 was in a joint.. dope......   \n",
       "29309    Negative  @EA @PlayApex I’ve been trying to play the gam...   \n",
       "29737     Neutral  I really had a bad feeling to clear ppl with t...   \n",
       "45666     Neutral                       Eh.   Probably for the best.   \n",
       "48357    Positive  When the State tells you you safe to go to Hom...   \n",
       "\n",
       "       Sentiment_target  \n",
       "56570                 0  \n",
       "54194                 2  \n",
       "64054                 2  \n",
       "42756                 3  \n",
       "38295                 2  \n",
       "...                 ...  \n",
       "18029                 0  \n",
       "29309                 2  \n",
       "29737                 1  \n",
       "45666                 1  \n",
       "48357                 0  \n",
       "\n",
       "[35000 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_df['Sentiment_target'] = labeled_df.Sentiment.factorize()[0]\n",
    "# short_df=short_df[['Tweet', 'Sentiment_target']].drop_duplicates()\n",
    "short_df = labeled_df.sample(35000, random_state=1).copy()\n",
    "short_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "682111ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.143144Z",
     "iopub.status.busy": "2022-02-20T21:04:58.142410Z",
     "iopub.status.idle": "2022-02-20T21:04:58.186828Z",
     "shell.execute_reply": "2022-02-20T21:04:58.186131Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.007695Z"
    },
    "papermill": {
     "duration": 0.095653,
     "end_time": "2022-02-20T21:04:58.186972",
     "exception": false,
     "start_time": "2022-02-20T21:04:58.091319",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Sentiment</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentiment_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56570</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Are we just going to pretend to be @ Ubisoft a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54194</th>\n",
       "      <td>Negative</td>\n",
       "      <td>The problem is even more than racism, it's a d...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64054</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Hey @EAMaddenNFL but when else is the update c...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42756</th>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>Can Mingyu also play PUBG huhuhu I want him to...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38295</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@BlizzardCSEU_EN Any logical reason why my hea...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18029</th>\n",
       "      <td>Positive</td>\n",
       "      <td>U could hear that ps1 was in a joint.. dope......</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29309</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@EA @PlayApex I’ve been trying to play the gam...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29737</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>I really had a bad feeling to clear ppl with t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45666</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>Eh.   Probably for the best.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48357</th>\n",
       "      <td>Positive</td>\n",
       "      <td>When the State tells you you safe to go to Hom...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment                                              Tweet  \\\n",
       "56570    Positive  Are we just going to pretend to be @ Ubisoft a...   \n",
       "54194    Negative  The problem is even more than racism, it's a d...   \n",
       "64054    Negative  Hey @EAMaddenNFL but when else is the update c...   \n",
       "42756  Irrelevant  Can Mingyu also play PUBG huhuhu I want him to...   \n",
       "38295    Negative  @BlizzardCSEU_EN Any logical reason why my hea...   \n",
       "...           ...                                                ...   \n",
       "18029    Positive  U could hear that ps1 was in a joint.. dope......   \n",
       "29309    Negative  @EA @PlayApex I’ve been trying to play the gam...   \n",
       "29737     Neutral  I really had a bad feeling to clear ppl with t...   \n",
       "45666     Neutral                       Eh.   Probably for the best.   \n",
       "48357    Positive  When the State tells you you safe to go to Hom...   \n",
       "\n",
       "       Sentiment_target  \n",
       "56570                 0  \n",
       "54194                 2  \n",
       "64054                 2  \n",
       "42756                 3  \n",
       "38295                 2  \n",
       "...                 ...  \n",
       "18029                 0  \n",
       "29309                 2  \n",
       "29737                 1  \n",
       "45666                 1  \n",
       "48357                 0  \n",
       "\n",
       "[33677 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_df=short_df.drop_duplicates()\n",
    "short_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92c895b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.288895Z",
     "iopub.status.busy": "2022-02-20T21:04:58.287805Z",
     "iopub.status.idle": "2022-02-20T21:04:58.300916Z",
     "shell.execute_reply": "2022-02-20T21:04:58.300333Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.05366Z"
    },
    "papermill": {
     "duration": 0.06538,
     "end_time": "2022-02-20T21:04:58.301064",
     "exception": false,
     "start_time": "2022-02-20T21:04:58.235684",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sentiment</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentiment_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Are we just going to pretend to be @ Ubisoft a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Negative</td>\n",
       "      <td>The problem is even more than racism, it's a d...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Hey @EAMaddenNFL but when else is the update c...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>Can Mingyu also play PUBG huhuhu I want him to...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@BlizzardCSEU_EN Any logical reason why my hea...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33672</th>\n",
       "      <td>Positive</td>\n",
       "      <td>U could hear that ps1 was in a joint.. dope......</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33673</th>\n",
       "      <td>Negative</td>\n",
       "      <td>@EA @PlayApex I’ve been trying to play the gam...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33674</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>I really had a bad feeling to clear ppl with t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33675</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>Eh.   Probably for the best.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33676</th>\n",
       "      <td>Positive</td>\n",
       "      <td>When the State tells you you safe to go to Hom...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment                                              Tweet  \\\n",
       "0        Positive  Are we just going to pretend to be @ Ubisoft a...   \n",
       "1        Negative  The problem is even more than racism, it's a d...   \n",
       "2        Negative  Hey @EAMaddenNFL but when else is the update c...   \n",
       "3      Irrelevant  Can Mingyu also play PUBG huhuhu I want him to...   \n",
       "4        Negative  @BlizzardCSEU_EN Any logical reason why my hea...   \n",
       "...           ...                                                ...   \n",
       "33672    Positive  U could hear that ps1 was in a joint.. dope......   \n",
       "33673    Negative  @EA @PlayApex I’ve been trying to play the gam...   \n",
       "33674     Neutral  I really had a bad feeling to clear ppl with t...   \n",
       "33675     Neutral                       Eh.   Probably for the best.   \n",
       "33676    Positive  When the State tells you you safe to go to Hom...   \n",
       "\n",
       "       Sentiment_target  \n",
       "0                     0  \n",
       "1                     2  \n",
       "2                     2  \n",
       "3                     3  \n",
       "4                     2  \n",
       "...                 ...  \n",
       "33672                 0  \n",
       "33673                 2  \n",
       "33674                 1  \n",
       "33675                 1  \n",
       "33676                 0  \n",
       "\n",
       "[33677 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_df = short_df.reset_index(drop=True)\n",
    "short_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51dc2db9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.401316Z",
     "iopub.status.busy": "2022-02-20T21:04:58.400243Z",
     "iopub.status.idle": "2022-02-20T21:04:58.406447Z",
     "shell.execute_reply": "2022-02-20T21:04:58.405884Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.069587Z"
    },
    "papermill": {
     "duration": 0.058452,
     "end_time": "2022-02-20T21:04:58.406639",
     "exception": false,
     "start_time": "2022-02-20T21:04:58.348187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    3,     7,    13, ..., 33663, 33666, 33667]),)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nonzero((short_df.Sentiment_target==3).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6fd9e9e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.507636Z",
     "iopub.status.busy": "2022-02-20T21:04:58.506455Z",
     "iopub.status.idle": "2022-02-20T21:04:58.515100Z",
     "shell.execute_reply": "2022-02-20T21:04:58.515840Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.078475Z"
    },
    "papermill": {
     "duration": 0.061976,
     "end_time": "2022-02-20T21:04:58.516075",
     "exception": false,
     "start_time": "2022-02-20T21:04:58.454099",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    14173\n",
       "2    10184\n",
       "0     9320\n",
       "Name: Sentiment_target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in np.nonzero((short_df.Sentiment_target==3).values):\n",
    "    short_df.Sentiment_target[i] = int(1)\n",
    "short_df.Sentiment_target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e312b95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:58.623089Z",
     "iopub.status.busy": "2022-02-20T21:04:58.617496Z",
     "iopub.status.idle": "2022-02-20T21:04:58.952007Z",
     "shell.execute_reply": "2022-02-20T21:04:58.951189Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.099093Z"
    },
    "papermill": {
     "duration": 0.386649,
     "end_time": "2022-02-20T21:04:58.952167",
     "exception": false,
     "start_time": "2022-02-20T21:04:58.565518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "colors = ['lightgrey','lightblue','darkgreen', 'darkred']\n",
    "short_df.groupby(['Sentiment']).Tweet.count().sort_values().plot.barh(\n",
    "    ylim=0, color=colors, title= 'NUMBER OF TWEETS IN EACH SENTIMENT CATEGORY\\n')\n",
    "plt.xlabel('Number of ocurrences', fontsize = 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d94670a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:59.069307Z",
     "iopub.status.busy": "2022-02-20T21:04:59.068570Z",
     "iopub.status.idle": "2022-02-20T21:04:59.362761Z",
     "shell.execute_reply": "2022-02-20T21:04:59.361866Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.358129Z"
    },
    "papermill": {
     "duration": 0.358511,
     "end_time": "2022-02-20T21:04:59.362983",
     "exception": false,
     "start_time": "2022-02-20T21:04:59.004472",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "colors = ['darkgreen', 'darkred', 'lightblue']\n",
    "short_df.groupby(['Sentiment_target']).Tweet.count().sort_values().plot.barh(\n",
    "    ylim=0, color=colors, title= 'NUMBER OF TWEETS IN EACH SENTIMENT CATEGORY\\n')\n",
    "plt.xlabel('Number of ocurrences', fontsize = 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99111743",
   "metadata": {
    "papermill": {
     "duration": 0.063937,
     "end_time": "2022-02-20T21:04:59.485569",
     "exception": false,
     "start_time": "2022-02-20T21:04:59.421632",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32652c95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:04:59.614089Z",
     "iopub.status.busy": "2022-02-20T21:04:59.613012Z",
     "iopub.status.idle": "2022-02-20T21:05:02.750855Z",
     "shell.execute_reply": "2022-02-20T21:05:02.751340Z",
     "shell.execute_reply.started": "2022-02-19T20:31:53.617988Z"
    },
    "papermill": {
     "duration": 3.21115,
     "end_time": "2022-02-20T21:05:02.751552",
     "exception": false,
     "start_time": "2022-02-20T21:04:59.540402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Each of the 33677 tweets is represented by 14348 features (TF-IDF score of unigrams and bigrams)\n"
     ]
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5,\n",
    "                        ngram_range=(1, 2), \n",
    "                        stop_words='english')\n",
    "\n",
    "features = tfidf.fit_transform(short_df.Tweet).toarray()\n",
    "\n",
    "labels = short_df.Sentiment_target\n",
    "\n",
    "print(\"Each of the %d tweets is represented by %d features (TF-IDF score of unigrams and bigrams)\" %(features.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a5b087c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:05:02.864178Z",
     "iopub.status.busy": "2022-02-20T21:05:02.859898Z",
     "iopub.status.idle": "2022-02-20T21:05:02.868144Z",
     "shell.execute_reply": "2022-02-20T21:05:02.868640Z",
     "shell.execute_reply.started": "2022-02-19T20:35:10.55358Z"
    },
    "papermill": {
     "duration": 0.064577,
     "end_time": "2022-02-20T21:05:02.868844",
     "exception": false,
     "start_time": "2022-02-20T21:05:02.804267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        2\n",
       "2        2\n",
       "3        1\n",
       "4        2\n",
       "        ..\n",
       "33672    0\n",
       "33673    2\n",
       "33674    1\n",
       "33675    1\n",
       "33676    0\n",
       "Name: Sentiment_target, Length: 33677, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb93b6b6",
   "metadata": {
    "papermill": {
     "duration": 0.053618,
     "end_time": "2022-02-20T21:05:02.974746",
     "exception": false,
     "start_time": "2022-02-20T21:05:02.921128",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef181ce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:05:03.092367Z",
     "iopub.status.busy": "2022-02-20T21:05:03.091302Z",
     "iopub.status.idle": "2022-02-20T21:05:03.106782Z",
     "shell.execute_reply": "2022-02-20T21:05:03.107611Z",
     "shell.execute_reply.started": "2022-02-19T20:36:18.90246Z"
    },
    "papermill": {
     "duration": 0.080394,
     "end_time": "2022-02-20T21:05:03.107899",
     "exception": false,
     "start_time": "2022-02-20T21:05:03.027505",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = short_df['Tweet'] \n",
    "y = short_df['Sentiment_target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e40622aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:05:03.237778Z",
     "iopub.status.busy": "2022-02-20T21:05:03.237030Z",
     "iopub.status.idle": "2022-02-20T21:05:03.243130Z",
     "shell.execute_reply": "2022-02-20T21:05:03.243665Z",
     "shell.execute_reply.started": "2022-02-19T20:36:28.276229Z"
    },
    "papermill": {
     "duration": 0.06432,
     "end_time": "2022-02-20T21:05:03.243859",
     "exception": false,
     "start_time": "2022-02-20T21:05:03.179539",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10470    So shit went sideways with  . . twitch.tv/sa7a...\n",
       "27382    Judge blocks Microsoft's Pentagon contract aft...\n",
       "21013    Interesting... (for real, Jessica is pretty aw...\n",
       "11684    U could hear this PS1 sound in this joint.. do...\n",
       "25306    Borderlands 3 Chapter 1 youtu.be / 0SKu6Vr4iXU...\n",
       "                               ...                        \n",
       "20757    I appreciate how my favorite sniper stays on t...\n",
       "32103                                             music . \n",
       "30403    Kena is named one in my top 5 most anticipated...\n",
       "21243    After questionable recent actions, I've really...\n",
       "2732                                          ... Shut up.\n",
       "Name: Tweet, Length: 25257, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86182e65",
   "metadata": {
    "papermill": {
     "duration": 0.053956,
     "end_time": "2022-02-20T21:05:03.350719",
     "exception": false,
     "start_time": "2022-02-20T21:05:03.296763",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Building models: RandomForest, LinearSVM, MultinomialNB, LogRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4b8b256",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:05:03.461119Z",
     "iopub.status.busy": "2022-02-20T21:05:03.460127Z",
     "iopub.status.idle": "2022-02-20T21:05:03.465846Z",
     "shell.execute_reply": "2022-02-20T21:05:03.465276Z",
     "shell.execute_reply.started": "2022-02-19T20:37:15.885447Z"
    },
    "papermill": {
     "duration": 0.061133,
     "end_time": "2022-02-20T21:05:03.466001",
     "exception": false,
     "start_time": "2022-02-20T21:05:03.404868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = [\n",
    "    RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0, verbose=1),\n",
    "    LinearSVC(verbose=1),\n",
    "    MultinomialNB(),\n",
    "    LogisticRegression(random_state=0,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd1b3f27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:05:03.581512Z",
     "iopub.status.busy": "2022-02-20T21:05:03.580357Z",
     "iopub.status.idle": "2022-02-20T21:17:17.153151Z",
     "shell.execute_reply": "2022-02-20T21:17:17.147604Z",
     "shell.execute_reply.started": "2022-02-19T20:37:48.386901Z"
    },
    "papermill": {
     "duration": 733.63554,
     "end_time": "2022-02-20T21:17:17.153464",
     "exception": false,
     "start_time": "2022-02-20T21:05:03.517924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcb404cebdaf441a902d9472d15b0909",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.6s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.7s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.6s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.8s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.7s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear].....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -7601.905350\n",
      "nSV = 18159\n",
      ".....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -9751.443377\n",
      "nSV = 21201\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -6923.355972\n",
      "nSV = 17460\n",
      "[LibLinear].....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -7742.776306\n",
      "nSV = 18385\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -9761.365051\n",
      "nSV = 21260\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -6941.475694\n",
      "nSV = 17356\n",
      "[LibLinear]....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -7726.085421\n",
      "nSV = 18468\n",
      ".....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -9871.445513\n",
      "nSV = 21348\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -7035.370240\n",
      "nSV = 17567\n",
      "[LibLinear].....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -7774.913294\n",
      "nSV = 18420\n",
      ".....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -9898.363767\n",
      "nSV = 21333\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -7041.623611\n",
      "nSV = 17582\n",
      "[LibLinear].....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -7737.664258\n",
      "nSV = 18403\n",
      ".....*\n",
      "optimization finished, #iter = 51\n",
      "Objective value = -9796.922558\n",
      "nSV = 21313\n",
      "....*.\n",
      "optimization finished, #iter = 50\n",
      "Objective value = -7014.116781\n",
      "nSV = 17603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =        43047     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.95977D+04    |proj g|=  2.35767D+03\n",
      "\n",
      "At iterate   50    f=  1.69984D+04    |proj g|=  6.29039D+01\n",
      "\n",
      "At iterate  100    f=  1.68935D+04    |proj g|=  1.05131D+01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "43047    100    111      1     0     0   1.051D+01   1.689D+04\n",
      "  F =   16893.453757012674     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =        43047     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.95977D+04    |proj g|=  2.35767D+03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   50    f=  1.70068D+04    |proj g|=  2.84529D+01\n",
      "\n",
      "At iterate  100    f=  1.69412D+04    |proj g|=  2.52823D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "43047    100    117      1     0     0   2.528D+00   1.694D+04\n",
      "  F =   16941.163475536974     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =        43047     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.95988D+04    |proj g|=  2.35833D+03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   50    f=  1.72785D+04    |proj g|=  2.91217D+01\n",
      "\n",
      "At iterate  100    f=  1.70644D+04    |proj g|=  4.67407D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "43047    100    112      1     0     0   4.674D+00   1.706D+04\n",
      "  F =   17064.388321862596     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =        43047     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.95988D+04    |proj g|=  2.35833D+03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   50    f=  1.71781D+04    |proj g|=  4.48982D+01\n",
      "\n",
      "At iterate  100    f=  1.70333D+04    |proj g|=  8.62855D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "43047    100    115      1     0     0   8.629D+00   1.703D+04\n",
      "  F =   17033.283688430423     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =        43047     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.95988D+04    |proj g|=  2.35733D+03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   50    f=  1.71069D+04    |proj g|=  1.36373D+02\n",
      "\n",
      "At iterate  100    f=  1.70057D+04    |proj g|=  9.77222D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "43047    100    110      1     0     0   9.772D+00   1.701D+04\n",
      "  F =   17005.664790110150     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    }
   ],
   "source": [
    "CV = 5\n",
    "cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
    "\n",
    "entries = []\n",
    "for model in tn(models):\n",
    "  model_name = model.__class__.__name__\n",
    "  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
    "  for fold_idx, accuracy in enumerate(accuracies):\n",
    "    entries.append((model_name, fold_idx, accuracy))\n",
    "    \n",
    "cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1850bd",
   "metadata": {
    "papermill": {
     "duration": 0.079247,
     "end_time": "2022-02-20T21:17:17.314786",
     "exception": false,
     "start_time": "2022-02-20T21:17:17.235539",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b1da5311",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:17.479349Z",
     "iopub.status.busy": "2022-02-20T21:17:17.478676Z",
     "iopub.status.idle": "2022-02-20T21:17:17.493743Z",
     "shell.execute_reply": "2022-02-20T21:17:17.494365Z",
     "shell.execute_reply.started": "2022-02-19T20:53:14.506263Z"
    },
    "papermill": {
     "duration": 0.101992,
     "end_time": "2022-02-20T21:17:17.494547",
     "exception": false,
     "start_time": "2022-02-20T21:17:17.392555",
     "status": "completed"
    },
    "tags": []
   },
   "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>Mean Accuracy</th>\n",
       "      <th>Standard deviation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.808297</td>\n",
       "      <td>0.008125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultinomialNB</th>\n",
       "      <td>0.738665</td>\n",
       "      <td>0.008320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.424147</td>\n",
       "      <td>0.001681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Mean Accuracy  Standard deviation\n",
       "model_name                                               \n",
       "LinearSVC                    0.808297            0.008125\n",
       "LogisticRegression                NaN                 NaN\n",
       "MultinomialNB                0.738665            0.008320\n",
       "RandomForestClassifier       0.424147            0.001681"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_accuracy = cv_df.groupby('model_name').accuracy.mean()\n",
    "std_accuracy = cv_df.groupby('model_name').accuracy.std()\n",
    "\n",
    "acc = pd.concat([mean_accuracy, std_accuracy], axis= 1, \n",
    "          ignore_index=True)\n",
    "acc.columns = ['Mean Accuracy', 'Standard deviation']\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c06bffb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:17.654835Z",
     "iopub.status.busy": "2022-02-20T21:17:17.654150Z",
     "iopub.status.idle": "2022-02-20T21:17:17.899152Z",
     "shell.execute_reply": "2022-02-20T21:17:17.899732Z",
     "shell.execute_reply.started": "2022-02-19T20:53:17.13943Z"
    },
    "papermill": {
     "duration": 0.328802,
     "end_time": "2022-02-20T21:17:17.899923",
     "exception": false,
     "start_time": "2022-02-20T21:17:17.571121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(x='model_name', y='accuracy', \n",
    "            data=cv_df, \n",
    "            color='lightblue', \n",
    "            showmeans=True)\n",
    "plt.title(\"MEAN ACCURACY (cv = 5)\\n\", size=14);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db196372",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:18.062419Z",
     "iopub.status.busy": "2022-02-20T21:17:18.061743Z",
     "iopub.status.idle": "2022-02-20T21:17:21.428507Z",
     "shell.execute_reply": "2022-02-20T21:17:21.430118Z",
     "shell.execute_reply.started": "2022-02-19T20:53:38.626478Z"
    },
    "papermill": {
     "duration": 3.450733,
     "end_time": "2022-02-20T21:17:21.430480",
     "exception": false,
     "start_time": "2022-02-20T21:17:17.979747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test,indices_train,indices_test = train_test_split(features, \n",
    "                                                               labels, \n",
    "                                                               short_df.index, test_size=0.25, \n",
    "                                                               random_state=1)\n",
    "model = LinearSVC()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "342b57e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:21.651724Z",
     "iopub.status.busy": "2022-02-20T21:17:21.650983Z",
     "iopub.status.idle": "2022-02-20T21:17:21.655173Z",
     "shell.execute_reply": "2022-02-20T21:17:21.655980Z",
     "shell.execute_reply.started": "2022-02-19T20:57:15.150107Z"
    },
    "papermill": {
     "duration": 0.090387,
     "end_time": "2022-02-20T21:17:21.656191",
     "exception": false,
     "start_time": "2022-02-20T21:17:21.565804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       0\n",
       " 1       1\n",
       " 2       2\n",
       " 3       2\n",
       " 4       1\n",
       "        ..\n",
       " 8415    0\n",
       " 8416    2\n",
       " 8417    1\n",
       " 8418    2\n",
       " 8419    1\n",
       " Name: Sentiment_target, Length: 8420, dtype: int64,\n",
       " array([1, 1, 2, ..., 1, 1, 1]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = y_test.reset_index(drop=True)\n",
    "y_test, y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84cc00c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:21.832012Z",
     "iopub.status.busy": "2022-02-20T21:17:21.830977Z",
     "iopub.status.idle": "2022-02-20T21:17:21.836942Z",
     "shell.execute_reply": "2022-02-20T21:17:21.837516Z",
     "shell.execute_reply.started": "2022-02-19T20:58:18.463958Z"
    },
    "papermill": {
     "duration": 0.094395,
     "end_time": "2022-02-20T21:17:21.837719",
     "exception": false,
     "start_time": "2022-02-20T21:17:21.743324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8420, 8420)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test),len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e841772c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:22.003428Z",
     "iopub.status.busy": "2022-02-20T21:17:22.002649Z",
     "iopub.status.idle": "2022-02-20T21:17:22.024308Z",
     "shell.execute_reply": "2022-02-20T21:17:22.025041Z",
     "shell.execute_reply.started": "2022-02-19T20:59:23.359147Z"
    },
    "papermill": {
     "duration": 0.105173,
     "end_time": "2022-02-20T21:17:22.025240",
     "exception": false,
     "start_time": "2022-02-20T21:17:21.920067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\tCLASSIFICATIION METRICS\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.79      0.75      0.77      2274\n",
      "    Negative       0.80      0.83      0.81      3598\n",
      "     Neutral       0.82      0.81      0.81      2548\n",
      "\n",
      "    accuracy                           0.80      8420\n",
      "   macro avg       0.80      0.80      0.80      8420\n",
      "weighted avg       0.80      0.80      0.80      8420\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\t\\t\\t\\tCLASSIFICATIION METRICS\\n')\n",
    "print(metrics.classification_report(y_test, y_pred, target_names=['Positive','Negative', 'Neutral']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c860653",
   "metadata": {
    "papermill": {
     "duration": 0.081952,
     "end_time": "2022-02-20T21:17:22.191180",
     "exception": false,
     "start_time": "2022-02-20T21:17:22.109228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "156f7d60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:22.359246Z",
     "iopub.status.busy": "2022-02-20T21:17:22.356158Z",
     "iopub.status.idle": "2022-02-20T21:17:23.978824Z",
     "shell.execute_reply": "2022-02-20T21:17:23.978239Z",
     "shell.execute_reply.started": "2022-02-19T21:00:20.959243Z"
    },
    "papermill": {
     "duration": 1.707763,
     "end_time": "2022-02-20T21:17:23.978972",
     "exception": false,
     "start_time": "2022-02-20T21:17:22.271209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'confusion_matrix'...\r\n",
      "remote: Enumerating objects: 15, done.\u001b[K\r\n",
      "remote: Total 15 (delta 0), reused 0 (delta 0), pack-reused 15\u001b[K\r\n",
      "Unpacking objects: 100% (15/15), 119.69 KiB | 3.32 MiB/s, done.\r\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/DTrimarchi10/confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff256857",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:24.144462Z",
     "iopub.status.busy": "2022-02-20T21:17:24.143413Z",
     "iopub.status.idle": "2022-02-20T21:17:25.282945Z",
     "shell.execute_reply": "2022-02-20T21:17:25.283659Z",
     "shell.execute_reply.started": "2022-02-19T21:00:24.192068Z"
    },
    "papermill": {
     "duration": 1.223764,
     "end_time": "2022-02-20T21:17:25.283870",
     "exception": false,
     "start_time": "2022-02-20T21:17:24.060106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-02-20 21:17:24--  https://raw.githubusercontent.com/DTrimarchi10/confusion_matrix/master/cf_matrix.py\r\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 4088 (4.0K) [text/plain]\r\n",
      "Failed to rename cf_matrix.py to cf_matrix.py.1: (2) No such file or directory\r\n",
      "Saving to: ‘cf_matrix.py’\r\n",
      "\r\n",
      "cf_matrix.py        100%[===================>]   3.99K  --.-KB/s    in 0s      \r\n",
      "\r\n",
      "2022-02-20 21:17:25 (36.0 MB/s) - ‘cf_matrix.py’ saved [4088/4088]\r\n",
      "\r\n",
      "--2022-02-20 21:17:25--  https://raw.githubusercontent.com/DTrimarchi10/confusion_matrix/master/cf_matrix.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4088 (4.0K) [text/plain]\n",
      "Saving to: ‘cf_matrix.py’\n",
      "\n",
      "     0K ...                                                   100% 16.3M=0s\n",
      "\n",
      "2022-02-20 21:17:25 (16.3 MB/s) - ‘cf_matrix.py’ saved [4088/4088]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "url = f\"https://raw.githubusercontent.com/DTrimarchi10/confusion_matrix/master/cf_matrix.py\"\n",
    "!wget --no-cache --backups=1 {url}\n",
    "import subprocess\n",
    "result = subprocess.run([\"wget\", \"--no-cache\", \"--backups=1\", url], stderr=subprocess.PIPE, stdout=subprocess.PIPE)\n",
    "print(result.stderr.decode(\"utf-8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "251edf28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:25.460613Z",
     "iopub.status.busy": "2022-02-20T21:17:25.459879Z",
     "iopub.status.idle": "2022-02-20T21:17:25.466498Z",
     "shell.execute_reply": "2022-02-20T21:17:25.467095Z",
     "shell.execute_reply.started": "2022-02-19T21:00:29.090595Z"
    },
    "papermill": {
     "duration": 0.094191,
     "end_time": "2022-02-20T21:17:25.467284",
     "exception": false,
     "start_time": "2022-02-20T21:17:25.373093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from cf_matrix import make_confusion_matrix\n",
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26cec71d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:25.640143Z",
     "iopub.status.busy": "2022-02-20T21:17:25.639443Z",
     "iopub.status.idle": "2022-02-20T21:17:25.656080Z",
     "shell.execute_reply": "2022-02-20T21:17:25.656732Z",
     "shell.execute_reply.started": "2022-02-19T21:01:10.38479Z"
    },
    "papermill": {
     "duration": 0.105153,
     "end_time": "2022-02-20T21:17:25.656922",
     "exception": false,
     "start_time": "2022-02-20T21:17:25.551769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1702,  420,  152],\n",
       "       [ 315, 2994,  289],\n",
       "       [ 148,  348, 2052]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(y_test, y_pred)\n",
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5a2a3395",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:25.833226Z",
     "iopub.status.busy": "2022-02-20T21:17:25.832453Z",
     "iopub.status.idle": "2022-02-20T21:17:26.089374Z",
     "shell.execute_reply": "2022-02-20T21:17:26.089954Z",
     "shell.execute_reply.started": "2022-02-19T21:00:38.212962Z"
    },
    "papermill": {
     "duration": 0.346391,
     "end_time": "2022-02-20T21:17:26.090141",
     "exception": false,
     "start_time": "2022-02-20T21:17:25.743750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Positive','Negative', 'Neutral']\n",
    "make_confusion_matrix(conf_mat, figsize=(8,6), cbar=False,categories = categories, title='Tweet Sentiments CF Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412c053e",
   "metadata": {
    "papermill": {
     "duration": 0.084712,
     "end_time": "2022-02-20T21:17:26.264353",
     "exception": false,
     "start_time": "2022-02-20T21:17:26.179641",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b34ac802",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:26.443400Z",
     "iopub.status.busy": "2022-02-20T21:17:26.442699Z",
     "iopub.status.idle": "2022-02-20T21:17:29.304093Z",
     "shell.execute_reply": "2022-02-20T21:17:29.303440Z",
     "shell.execute_reply.started": "2022-02-19T21:03:04.748614Z"
    },
    "papermill": {
     "duration": 2.955314,
     "end_time": "2022-02-20T21:17:29.304265",
     "exception": false,
     "start_time": "2022-02-20T21:17:26.348951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state = 0)\n",
    "\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5,\n",
    "                        ngram_range=(1, 2), \n",
    "                        stop_words='english')\n",
    "\n",
    "fitted_vectorizer = tfidf.fit(X_train)\n",
    "tfidf_vectorizer_vectors = fitted_vectorizer.transform(X_train)\n",
    "\n",
    "model = LinearSVC().fit(tfidf_vectorizer_vectors, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a97654d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:29.486195Z",
     "iopub.status.busy": "2022-02-20T21:17:29.485448Z",
     "iopub.status.idle": "2022-02-20T21:17:29.490408Z",
     "shell.execute_reply": "2022-02-20T21:17:29.489724Z",
     "shell.execute_reply.started": "2022-02-19T21:07:54.506064Z"
    },
    "papermill": {
     "duration": 0.099787,
     "end_time": "2022-02-20T21:17:29.490560",
     "exception": false,
     "start_time": "2022-02-20T21:17:29.390773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Former Arsenal and West Ham player Alex Song has taken his case to FIFA after being sacked by FC Sion.\n",
      "\n",
      "The 32-year-old was one of nine players to lose his job when the Swiss side proposed reducing the wage bill of the first team as an emergency measure during the coronavirus\n"
     ]
    }
   ],
   "source": [
    "new_tweet = valid.Tweet[random.randint(0, len(valid.Tweet))]\n",
    "print(new_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "519b4279",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:29.671564Z",
     "iopub.status.busy": "2022-02-20T21:17:29.670861Z",
     "iopub.status.idle": "2022-02-20T21:17:29.680149Z",
     "shell.execute_reply": "2022-02-20T21:17:29.679336Z",
     "shell.execute_reply.started": "2022-02-19T21:07:57.474824Z"
    },
    "papermill": {
     "duration": 0.101143,
     "end_time": "2022-02-20T21:17:29.680306",
     "exception": false,
     "start_time": "2022-02-20T21:17:29.579163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(fitted_vectorizer.transform([new_tweet]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05359670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T21:17:29.873214Z",
     "iopub.status.busy": "2022-02-20T21:17:29.862419Z",
     "iopub.status.idle": "2022-02-20T21:17:29.876382Z",
     "shell.execute_reply": "2022-02-20T21:17:29.877278Z",
     "shell.execute_reply.started": "2022-02-19T21:07:59.667463Z"
    },
    "papermill": {
     "duration": 0.109517,
     "end_time": "2022-02-20T21:17:29.877482",
     "exception": false,
     "start_time": "2022-02-20T21:17:29.767965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Entity</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>is_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>6055</td>\n",
       "      <td>FIFA</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>Former Arsenal and West Ham player Alex Song h...</td>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "       Id Entity   Sentiment  \\\n",
       "915  6055   FIFA  Irrelevant   \n",
       "\n",
       "                                                 Tweet  is_train  \n",
       "915  Former Arsenal and West Ham player Alex Song h...     False  "
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