kernel (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "8a7a31597570418ebd5ea632c2a70f069bb3e063"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import sklearn.linear_model\n",
    "import sklearn\n",
    "import sklearn.ensemble\n",
    "import sklearn.tree\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "N = 150000\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3a99531d8d9888d27f690b52ad0c5ee439c0fcd7"
   },
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "ec2e708e709d2195131b189a36f506fddf9fae6c"
   },
   "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>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhraseId  SentenceId                                             Phrase  \\\n",
       "0         1           1  A series of escapades demonstrating the adage ...   \n",
       "1         2           1  A series of escapades demonstrating the adage ...   \n",
       "2         3           1                                           A series   \n",
       "3         4           1                                                  A   \n",
       "4         5           1                                             series   \n",
       "\n",
       "   Sentiment  \n",
       "0          1  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data = pd.read_csv('../input/train.tsv',delimiter='\\t',encoding='utf-8')\n",
    "data = pd.read_csv('train.csv',delimiter='\\t',encoding='utf-8')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ca46f055da943032f9da52c6aa9041b21d5d73e6"
   },
   "source": [
    "# Cleaning Data\n",
    "###  Finding Features of our Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "6f8abd0fed8350e119af09ba6ae41979504f3839"
   },
   "outputs": [],
   "source": [
    "may_be_names = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']\n",
    "\n",
    "# removing all features (may be) containing names\n",
    "a = []\n",
    "for i in range(len(data.Phrase)-N):\n",
    "    line = data.Phrase[i].split(' ')\n",
    "    line_without_names = [word.lower() for word in line if word != '' and word[0] not in may_be_names]\n",
    "    a += list(set(line_without_names))\n",
    "    a = list(set(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "127dda7c7c2f22dffec09d68438b47d208f7516d"
   },
   "outputs": [],
   "source": [
    "# adding stop words\n",
    "a.sort()\n",
    "stop_words = set(stopwords.words('english'))\n",
    "new_stop_words = (list(stop_words)  + a[:121])\n",
    "new_stop_words.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "87e4189318f3eb0e1c4d4d437cfc0a70c3f46e7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features in our model :- 1292\n"
     ]
    }
   ],
   "source": [
    "# extract features (excluding stop words)\n",
    "features_of_model = [word for word in a if word not in new_stop_words]\n",
    "print('features in our model :-',len(features_of_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "52599dc2eeb8971751ca6c924169f1ca1a6c14c6"
   },
   "source": [
    "### Creating data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "9cc97c54d05f98828a46185b029e5ca34c9ed71d"
   },
   "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>base</th>\n",
       "      <th>baseball</th>\n",
       "      <th>bathtub</th>\n",
       "      <th>battle</th>\n",
       "      <th>beaten</th>\n",
       "      <th>beautiful</th>\n",
       "      <th>becomes</th>\n",
       "      <th>begging</th>\n",
       "      <th>believe</th>\n",
       "      <th>beneath</th>\n",
       "      <th>...</th>\n",
       "      <th>wrong</th>\n",
       "      <th>yarn</th>\n",
       "      <th>year</th>\n",
       "      <th>years</th>\n",
       "      <th>yes</th>\n",
       "      <th>yet</th>\n",
       "      <th>young</th>\n",
       "      <th>youth</th>\n",
       "      <th>zingers</th>\n",
       "      <th>SENTIMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1293 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   base  baseball  bathtub  battle  beaten  beautiful  becomes  begging  \\\n",
       "0     0         0        0       0       0          0        0        0   \n",
       "1     0         0        0       0       0          0        0        0   \n",
       "2     0         0        0       0       0          0        0        0   \n",
       "3     0         0        0       0       0          0        0        0   \n",
       "4     0         0        0       0       0          0        0        0   \n",
       "\n",
       "   believe  beneath    ...      wrong  yarn  year  years  yes  yet  young  \\\n",
       "0        0        0    ...          0     0     0      0    0    0      0   \n",
       "1        0        0    ...          0     0     0      0    0    0      0   \n",
       "2        0        0    ...          0     0     0      0    0    0      0   \n",
       "3        0        0    ...          0     0     0      0    0    0      0   \n",
       "4        0        0    ...          0     0     0      0    0    0      0   \n",
       "\n",
       "   youth  zingers  SENTIMENT  \n",
       "0      0        0          0  \n",
       "1      0        0          0  \n",
       "2      0        0          0  \n",
       "3      0        0          0  \n",
       "4      0        0          0  \n",
       "\n",
       "[5 rows x 1293 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating data frame with null values\n",
    "X = pd.DataFrame(data = np.zeros((len(data)-N,len(features_of_model) + 1),dtype=int),columns = features_of_model+['SENTIMENT'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "2730e05a98bdafedea9d7e1debccb8f41e922363"
   },
   "outputs": [],
   "source": [
    "# entring values in data frame\n",
    "for i in range(len(data) - N):\n",
    "    current_row = data.Phrase[i].split()\n",
    "    for word in current_row:\n",
    "        if len(current_row) > 0:\n",
    "            word = word.lower()\n",
    "            X['SENTIMENT'][i] = data.Sentiment[i]\n",
    "            if word in features_of_model:\n",
    "                X[word][i] +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8dd28029669eb3eda107ac8d0fc4c8c0bbded16d"
   },
   "source": [
    "### Shuffling data to run on models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "018fa09125cb591de30129e3ca03feb2008a7cac"
   },
   "outputs": [],
   "source": [
    "# shuffling data for training and testing\n",
    "X_train,X_test,Y_train,Y_test = sklearn.model_selection.train_test_split(X.drop(['SENTIMENT'],axis = 1),X['SENTIMENT'],test_size = 0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6060 entries, 0 to 6059\n",
      "Columns: 1293 entries, base to SENTIMENT\n",
      "dtypes: int32(1293)\n",
      "memory usage: 29.9 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c69d3ed42454fb0741c7ce23e736b657a157531a"
   },
   "source": [
    "# Using differenent classifiers to find the accuracy of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "11e9a707a366f86cf121079b046251f1a02a1f61"
   },
   "outputs": [],
   "source": [
    "# to create list of model scores\n",
    "name = []\n",
    "test_score = []\n",
    "train_score = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear = linear_model.LinearRegression()\n",
    "#linear.fit(X_train, Y_train)\n",
    "#train_score +=[linear.score(X_train, Y_train)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicted=linear.predict(X_test)\n",
    "#predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d5f6af5db3d5fda768feb3be491262a11b265235"
   },
   "source": [
    "### Using Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "a3bd46d741bc518fad25660fd9fe6bb7506a4acc"
   },
   "outputs": [],
   "source": [
    "decision_tree = sklearn.tree.DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train,Y_train)\n",
    "name += ['decision tree']\n",
    "train_score += [decision_tree.score(X_train,Y_train)]\n",
    "test_score += [decision_tree.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "fa0a3c30d6459ae6f709e3e689622c5582122fad"
   },
   "outputs": [],
   "source": [
    "dec_test_score = []\n",
    "dec_train_score = []\n",
    "for depth in range(1,20):\n",
    "    decision_tree = sklearn.tree.DecisionTreeClassifier(max_depth=depth)\n",
    "    decision_tree.fit(X_train,Y_train)\n",
    "    dec_train_score += [decision_tree.score(X_train,Y_train)]\n",
    "    dec_test_score+= [decision_tree.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "58f2857d8c8eda31a67621c00093b3363c3eef44"
   },
   "source": [
    "### Plotting LINE PLOT to see the relationship of score with depth of tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "d72f441a78f6cc66fedac0fb2e2bb63729c6e5a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x229685f80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dec_test_score,label = 'decision tree test')\n",
    "plt.plot(dec_train_score,label = 'decision tree train')\n",
    "plt.xlabel('depth')\n",
    "plt.ylabel('score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "21c583c8e2f6f2a12acec72a808116d5223667f9"
   },
   "source": [
    "### Using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "2cc78f6d1a20ed2df7b74242135b77cd03212019"
   },
   "outputs": [],
   "source": [
    "random_forest = sklearn.ensemble.RandomForestClassifier()\n",
    "random_forest.fit(X_train,Y_train)\n",
    "name += ['random_forest']\n",
    "train_score += [random_forest.score(X_train,Y_train)]\n",
    "test_score += [random_forest.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "24915846d3e4f740bd86c588babf418c54816017"
   },
   "source": [
    "### Using Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "74c76a832b8f0693f284b5b69bd543b219248523"
   },
   "outputs": [],
   "source": [
    "support_vector_machine = SVC()\n",
    "support_vector_machine.fit(X_train,Y_train)\n",
    "name += ['support vector machine']\n",
    "train_score += [support_vector_machine.score(X_train,Y_train)]\n",
    "test_score += [support_vector_machine.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "81102e20e04ca44ff7e96952cf25cd5c271455d7"
   },
   "source": [
    "### Using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "952cc7d9a4cab2f61c6a76e88d89734e56763681"
   },
   "outputs": [],
   "source": [
    "logistic_regression = LogisticRegression()\n",
    "logistic_regression.fit(X_train,Y_train)\n",
    "name += ['logistic regression']\n",
    "train_score += [logistic_regression.score(X_train,Y_train)]\n",
    "test_score += [logistic_regression.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "50fb41fdc381b4c26bfeef08b54aacf12313d85c"
   },
   "source": [
    "### Using K-Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "05ec8a6315e8bed7a1170d53a40ec185456bd64d"
   },
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, Y_train)\n",
    "name += ['k-nearest neighbour , here k = 3']\n",
    "train_score += [neigh.score(X_train,Y_train)]\n",
    "test_score += [neigh.score(X_test,Y_test)]\n",
    "\n",
    "neigh1 = KNeighborsClassifier(n_neighbors=10)\n",
    "neigh1.fit(X_train, Y_train)\n",
    "name += ['k-nearest neighbour , here k = 10']\n",
    "train_score += [neigh1.score(X_train,Y_train)]\n",
    "test_score += [neigh1.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Using Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(X_train, Y_train)\n",
    "train_score +=[model.score(X_train,Y_train)]\n",
    "name += ['Gaussian Naive Bayes']\n",
    "test_score += [model.score(X_test,Y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "79cdf1a2821162a4269077ceef79719fe93f4561"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22969494cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(name,test_score)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "0f56e2612797a1e0cfe319d9c28f436d00800927"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6336633663366337,\n",
       " 0.6397139713971397,\n",
       " 0.5808580858085809,\n",
       " 0.6600660066006601,\n",
       " 0.6303630363036303,\n",
       " 0.6303630363036303,\n",
       " 0.20737073707370737]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8847241867043847,\n",
       " 0.8745874587458746,\n",
       " 0.5730787364450731,\n",
       " 0.7454031117397454,\n",
       " 0.7814710042432814,\n",
       " 0.6603017444601603]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "736396ffb37301cceb88e98a6cc2e374f8607028"
   },
   "outputs": [],
   "source": [
    "Sentiment_words=[]\n",
    "for row in data['Sentiment']:\n",
    "    if row ==0:\n",
    "        Sentiment_words.append('negative')\n",
    "    elif row == 1:\n",
    "        Sentiment_words.append('somewhat negative')\n",
    "    elif row == 2:\n",
    "        Sentiment_words.append('neutral')\n",
    "    elif row == 3:\n",
    "        Sentiment_words.append('somewhat positive')\n",
    "    elif row == 4:\n",
    "        Sentiment_words.append('positive')\n",
    "    else:\n",
    "        Sentiment_words.append('Failed')\n",
    "data['Sentiment_words'] = Sentiment_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "a81240acbb1ffcc6f07ce622d4802d9545778425"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "somewhat positive    32927\n",
       "somewhat negative    27273\n",
       "positive              9206\n",
       "neutral              79582\n",
       "negative              7072\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count=pd.value_counts(data['Sentiment_words'].values, sort=False)\n",
    "word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "8041a7258270a5022f2b5a8ea9b9395173aa819c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22969443f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Index = [1,2,3,4,5]\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(Index,word_count,color = 'blue')\n",
    "plt.xticks(Index,['neutral','negative','somewhat negative','positive','somewhat positive'],rotation=45)\n",
    "plt.ylabel('word_count')\n",
    "plt.xlabel('word')\n",
    "plt.title('Count of Moods')\n",
    "plt.bar(Index, word_count)\n",
    "for a,b in zip(Index, word_count):\n",
    "    plt.text(a, b, str(b) ,color='green', fontweight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "411890d14d679a4e08128e941df153de53f1f67e"
   },
   "outputs": [],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "#import seaborn as sns\n",
    "#from wordcloud import WordCloud,STOPWORDS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive=data[data['Sentiment_words']==('positive')]\n",
    "words = ' '.join(positive['Phrase'])\n",
    "split_word = \" \".join([word for word in words.split()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wordcloud = WordCloud(stopwords=STOPWORDS,\n",
    "                      #background_color='black',\n",
    "                      #width=3000,\n",
    "                      #height=2500\n",
    "                     #).generate(split_word)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}