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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+twitter_sentiment.csv filter=lfs diff=lfs merge=lfs -text

Twitter_Sentiment_analysis.ipynb

@@ -0,0 +1,637 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Twitter Sentiment Analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "import pickle\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import pandas as pd "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>clean_text</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>when modi promised “minimum government maximum...</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>talk all the nonsense and continue all the dra...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>what did just say vote for modi  welcome bjp t...</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>asking his supporters prefix chowkidar their n...</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>answer who among these the most powerful world...</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          clean_text  category\n",
+       "0  when modi promised “minimum government maximum...      -1.0\n",
+       "1  talk all the nonsense and continue all the dra...       0.0\n",
+       "2  what did just say vote for modi  welcome bjp t...       1.0\n",
+       "3  asking his supporters prefix chowkidar their n...       1.0\n",
+       "4  answer who among these the most powerful world...       1.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('./Twitter_Data.csv' )\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(75682, 3)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**drop unnecessary columns**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[[2,3]].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will murder yo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>I am coming to the borders and I will kill you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will kill you ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im coming on borderlands and i will murder you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          2                                                  3\n",
+       "0  Positive  im getting on borderlands and i will murder yo...\n",
+       "1  Positive  I am coming to the borders and I will kill you...\n",
+       "2  Positive  im getting on borderlands and i will kill you ...\n",
+       "3  Positive  im coming on borderlands and i will murder you...\n",
+       "4  Positive  im getting on borderlands 2 and i will murder ..."
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>sentiments</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will murder yo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>I am coming to the borders and I will kill you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will kill you ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im coming on borderlands and i will murder you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sentiments                                               text\n",
+       "0   Positive  im getting on borderlands and i will murder yo...\n",
+       "1   Positive  I am coming to the borders and I will kill you...\n",
+       "2   Positive  im getting on borderlands and i will kill you ...\n",
+       "3   Positive  im coming on borderlands and i will murder you...\n",
+       "4   Positive  im getting on borderlands 2 and i will murder ..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df.columns = ['sentiments','text']\n",
+    "df.rename(columns={2 : \"sentiments\" , 3 : \"text\"} , inplace= True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 75682 entries, 0 to 75681\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   sentiments  75682 non-null  object\n",
+      " 1   text        74996 non-null  object\n",
+      "dtypes: object(2)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() # to see data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isna().sum()\n",
+    "df.dropna(inplace= True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>sentiments</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will murder yo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>I am coming to the borders and I will kill you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will kill you ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im coming on borderlands and i will murder you...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sentiments                                               text\n",
+       "0   Positive  im getting on borderlands and i will murder yo...\n",
+       "1   Positive  I am coming to the borders and I will kill you...\n",
+       "2   Positive  im getting on borderlands and i will kill you ...\n",
+       "3   Positive  im coming on borderlands and i will murder you...\n",
+       "4   Positive  im getting on borderlands 2 and i will murder ..."
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_text(text):\n",
+    "    text = text.lower()\n",
+    "    text = re.sub(f'http\\S+','',text)\n",
+    "    text = re.sub(r'@[a-zA-Z0-9_]+','',text)\n",
+    "    text = re.sub(r'#','',text)\n",
+    "    text = re.sub(r'[^a-zA-Z\\S]','',text)\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['clean_text'] = df['text'].apply(process_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>sentiments</th>\n",
+       "      <th>text</th>\n",
+       "      <th>clean_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will murder yo...</td>\n",
+       "      <td>imgettingonborderlandsandiwillmurderyouall,</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>I am coming to the borders and I will kill you...</td>\n",
+       "      <td>iamcomingtothebordersandiwillkillyouall,</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands and i will kill you ...</td>\n",
+       "      <td>imgettingonborderlandsandiwillkillyouall,</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im coming on borderlands and i will murder you...</td>\n",
+       "      <td>imcomingonborderlandsandiwillmurderyouall,</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Positive</td>\n",
+       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
+       "      <td>imgettingonborderlands2andiwillmurderyoumeall,</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sentiments                                               text  \\\n",
+       "0   Positive  im getting on borderlands and i will murder yo...   \n",
+       "1   Positive  I am coming to the borders and I will kill you...   \n",
+       "2   Positive  im getting on borderlands and i will kill you ...   \n",
+       "3   Positive  im coming on borderlands and i will murder you...   \n",
+       "4   Positive  im getting on borderlands 2 and i will murder ...   \n",
+       "\n",
+       "                                       clean_text  \n",
+       "0     imgettingonborderlandsandiwillmurderyouall,  \n",
+       "1        iamcomingtothebordersandiwillkillyouall,  \n",
+       "2       imgettingonborderlandsandiwillkillyouall,  \n",
+       "3      imcomingonborderlandsandiwillmurderyouall,  \n",
+       "4  imgettingonborderlands2andiwillmurderyoumeall,  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sentiments\n",
+       "Negative      22624\n",
+       "Positive      20932\n",
+       "Neutral       18393\n",
+       "Irrelevant    13047\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sentiments'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_vectorizer = CountVectorizer(max_features=5000)\n",
+    "count_matrix = count_vectorizer.fit_transform(df['clean_text'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train , X_test , y_train , y_test = train_test_split(count_matrix, df['clean_text'],test_size=0.2 , random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# nb_classifier = MultinomialNB()\n",
+    "# nb_classifier.fit(X_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_pred = nb_classifier.predict(X_test)\n",
+    "# accuracy = accuracy_score(y_test , y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Twitter_sentiment.py

@@ -0,0 +1,53 @@
+import re
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score
+import pandas as pd
+import warnings
+import pickle
+
+warnings.filterwarnings('ignore')
+
+# Load dataset
+df = pd.read_csv('./twitter_sentiment.csv', header=None, index_col=0)
+df = df[[2, 3]].reset_index(drop=True)
+df.rename(columns={2: "sentiments", 3: "text"}, inplace=True)
+
+# Drop missing values
+df.dropna(inplace=True)
+
+# Preprocess text
+def process_text(text):
+    text = text.lower()
+    text = re.sub(r'http\S+', '', text)
+    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
+    text = re.sub(r'#', '', text)
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    return text
+
+df['clean_text'] = df['text'].apply(process_text)
+
+# Vectorize text
+count_vectorizer = CountVectorizer(max_features=5000)
+count_matrix = count_vectorizer.fit_transform(df['clean_text'])
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(
+    count_matrix, df['sentiments'], test_size=0.2, random_state=42
+)
+
+# Train model
+nb_classifier = MultinomialNB()
+nb_classifier.fit(X_train, y_train)
+
+# Predict and evaluate
+y_pred = nb_classifier.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)
+
+with open('count_vectorizer.pkl','wb') as vectorizer_file:
+    pickle.dump(count_vectorizer , vectorizer_file)
+
+with open('nb_classifier.pkl','wb') as classifier_file:
+    pickle.dump(nb_classifier , classifier_file)

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import pickle
+import re
+from sklearn.feature_extraction.text import  CountVectorizer
+
+with open('count_vectorizer.pkl','rb')as vectorizer_file:
+    count_vectorizer = pickle.load(vectorizer_file)
+
+with open('nb_classifier.pkl','rb')as classifier_file:
+    nb_classifier = pickle.load(classifier_file)
+
+def process_text(text):
+    text = text.lower()
+    text = re.sub(r'http\S+', '', text)
+    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
+    text = re.sub(r'#', '', text)
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    return text
+
+sentiment_mapping = {
+        "Negative" : "Negative 😔",
+        "Positive" : "Positive 😊",
+        "Neutral" : "Neutral 🙄",
+        "Irrelevant" : "Irrelevant 🤷‍♂️"
+    }
+
+def main():
+    col1 , col2 , col3 ,col4 = st.columns([1,1,3,1])
+    with col3:
+        st.image("./image/pngwing.com (1).png" , width=100)
+    st.title("Twitter Sentiment Classifier")
+    st.write("Enter twitter tweet below :")
+    input_text = st.text_area("Input Text :","")
+    if st.button("Predict"):
+        cleaned_text = process_text(input_text)
+        vectorizer_text = count_vectorizer.transform([cleaned_text])
+        sentiment_prediction = nb_classifier.predict(vectorizer_text)[0]
+        
+        predicted_sentiment =  sentiment_mapping.get(sentiment_prediction , "Unknown Sentiment")
+
+        st.write("Predicted Sentimen :")
+        st.title(predicted_sentiment)
+
+
+if __name__  == "__main__":
+    main()

count_vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0283cf923e981e93aad9148b59bd974d6658ffb49fff332abd81bf0d5da693c6
+size 141069

nb_classifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:020b96fa1ba1660e47bf4634550a7f019a3487205c07aaa4b62efc02f9281ad2
+size 320788

twitter_sentiment.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17a94670bd8955bc3f5a740cd72797cd763c1ae5fdfea5eef619c1a28435c203
+size 10489426