{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLP and Supervised Learning\n",
    "## Classification of Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data\n",
    "\n",
    "Source: https://www.kaggle.com/crowdflower/twitter-airline-sentiment?select=Tweets.csv\n",
    "\n",
    "This data originally came from Crowdflower's Data for Everyone library.\n",
    "\n",
    "As the original source says,\n",
    "\n",
    "A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as \"late flight\" or \"rude service\").\n",
    "\n",
    "#### The Goal: Create a Machine Learning Algorithm that can predict if a tweet is positive, neutral, or negative. In the future we could use such an algorithm to automatically read and flag tweets for an airline for a customer service agent to reach out to contact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/airline_tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "               tweet_created tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='airline', ylabel='count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='airline',hue='airline_sentiment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='negativereason')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='airline_sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='airline_sentiment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative    9178\n",
       "neutral     3099\n",
       "positive    2363\n",
       "Name: airline_sentiment, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airline_sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features and Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['airline_sentiment','text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  airline_sentiment                                               text\n",
       "0           neutral                @VirginAmerica What @dhepburn said.\n",
       "1          positive  @VirginAmerica plus you've added commercials t...\n",
       "2           neutral  @VirginAmerica I didn't today... Must mean I n...\n",
       "3          negative  @VirginAmerica it's really aggressive to blast...\n",
       "4          negative  @VirginAmerica and it's a really big bad thing..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['airline_sentiment']\n",
    "X = df['text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(stop_words='english')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tfidf = tfidf.transform(X_train)\n",
    "X_test_tfidf = tfidf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<11712x12971 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 107073 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DO NOT USE .todense() for such a large sparse matrix!!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparisons - Naive Bayes,LogisticRegression, LinearSVC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()\n",
    "nb.fit(X_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log = LogisticRegression(max_iter=1000)\n",
    "log.fit(X_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "svc = LinearSVC()\n",
    "svc.fit(X_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report(model):\n",
    "    preds = model.predict(X_test_tfidf)\n",
    "    print(classification_report(y_test,preds))\n",
    "    plot_confusion_matrix(model,X_test_tfidf,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB MODEL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.66      0.99      0.79      1817\n",
      "     neutral       0.79      0.15      0.26       628\n",
      "    positive       0.89      0.14      0.24       483\n",
      "\n",
      "    accuracy                           0.67      2928\n",
      "   macro avg       0.78      0.43      0.43      2928\n",
      "weighted avg       0.73      0.67      0.59      2928\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"NB MODEL\")\n",
    "report(nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.80      0.93      0.86      1817\n",
      "     neutral       0.63      0.47      0.54       628\n",
      "    positive       0.82      0.58      0.68       483\n",
      "\n",
      "    accuracy                           0.77      2928\n",
      "   macro avg       0.75      0.66      0.69      2928\n",
      "weighted avg       0.77      0.77      0.76      2928\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Logistic Regression\")\n",
    "report(log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.82      0.89      0.86      1817\n",
      "     neutral       0.59      0.52      0.55       628\n",
      "    positive       0.76      0.64      0.69       483\n",
      "\n",
      "    accuracy                           0.77      2928\n",
      "   macro avg       0.73      0.68      0.70      2928\n",
      "weighted avg       0.76      0.77      0.77      2928\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('SVC')\n",
    "report(svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finalizing a PipeLine for Deployment on  New Tweets\n",
    "\n",
    "If we were satisfied with a model's performance, we should set up a pipeline that can take in a tweet directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('tfidf',TfidfVectorizer()),('svc',LinearSVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('svc', LinearSVC())])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(df['text'],df['airline_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['positive'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_tweet = ['good flight']\n",
    "pipe.predict(new_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['negative'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_tweet = ['bad flight']\n",
    "pipe.predict(new_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neutral'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_tweet = ['ok flight']\n",
    "pipe.predict(new_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}