nepali_sentiment_classifier-Emoji-decoded.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "3sgn7qAAvFcx"
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   "source": [
    "# Reading the train, test\n",
    "\n",
    "df_train = pd.read_csv('train.csv',encoding='utf-8')\n",
    "df_test = pd.read_csv('test.csv',encoding='utf-8')"
   ]
  },
  {
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       "                                                   text label\n",
       "0     बजार ले जसरी ट्रेन्ड चेन्ज गर्यो यो हेर्दा तत्...     2\n",
       "1     1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...     1\n",
       "2     होइन यो सानिमा बैंक ले bonus घोसणा गरेको २ महि...     2\n",
       "3     खैँ MBJC प्रति कित्तामा रू,10/-ले बढेर आज रू,1...     2\n",
       "4                                          राम्रो भयो️️     1\n",
       "...                                                 ...   ...\n",
       "5995  समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...     1\n",
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       "1     राम्रो कार्यक्रम, पहिलो सिजनले समेटेको कार्यक्...     1\n",
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       "4     बहसको सुरुवात भएको छ, अझै जोडदार रुपमा गर्नुपर...     1\n",
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,4))\n",
    "df_train['label'].value_counts().plot(kind='bar')\n",
    "plt.axis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BpAWfCVMvFu2",
    "outputId": "b3b95d39-fadb-4cdb-c0a5-ba97e0c8301b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['label'].value_counts().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df_train.dropna()\n",
    "df_test=df_test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Ktz2SOcNvFx2"
   },
   "outputs": [],
   "source": [
    "df_train.drop(df_train[df_train['label'] == '-'].index, inplace = True)\n",
    "df_train.drop(df_train[df_train['label'] == '20'].index, inplace = True)\n",
    "df_train.drop(df_train[df_train['label'] == '11'].index, inplace = True)\n",
    "df_train.drop(df_train[df_train['label'] == 'o'].index, inplace = True)\n",
    "df_train.drop(df_train[df_train['label'] == '--'].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "b226PLKTT6s4"
   },
   "outputs": [],
   "source": [
    "df_test.drop(df_test[df_test['label'] == '-'].index, inplace = True)\n",
    "df_test.drop(df_test[df_test['label'] == 'o'].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XPJ-WkOSUIeQ",
    "outputId": "64a2f1c8-e791-472b-fe00-aa900c762ae2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    888\n",
       "0    609\n",
       "2    496\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1dIMjMaGvF09",
    "outputId": "e04c9092-59b3-4629-b1ae-1613ad43065e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2378\n",
       "0    2376\n",
       "2    1236\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 262
    },
    "id": "c_HjjPehvF38",
    "outputId": "246ff727-3aa9-4dac-8403-18bc76aa9320"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rd9+f5P5FrwNYWVUd6e7di14HAGw2vkMBYHV8h8LmNO/LcRxPcvnM88umGQAAAAAAW9C8I/STSXZV1ZVV9f4kH09ycM5rAAAAAABgTuZ6OY7ufreq7k7yeJILkhzo7ufnuQZgXbl0DgCsju9QAFgd36GwCVV3L3oNAAAAAABsUfO+HAcAAAAAAO8hIjQAAAAAAMOI0AAAAAAADDPXGxMCAMB7UVX9dJI9SXZMo+NJDnb3C4tbFQBsfNN36I4kX+vu78zMb+7uryxuZcD58EtoYM2q6s5FrwEANqqq+q0kDyepJF+f/irJQ1W1f5FrA4CNrKp+PcmjSX4tyXNVtWdm9+8tZlXAalR3L3oNwCZXVf/e3Vcseh0AsBFV1b8m+VB3/+9p8/cneb67dy1mZQCwsVXVs0l+rru/U1U7k3wxyZ939x9X1T9394cXu0LgXLkcB3BOquqZ5XYl2T7PtQDAJvO9JD+e5OXT5pdO+wCAM3vfqUtwdPdLVXV9ki9W1Qdz8n9RYJMQoYFztT3JTUneOm1eSf5x/ssBgE3jk0kOV9WLSV6ZZlck+ckkdy9qUQCwCbxeVVd399NJMv0i+peSHEjyMwtdGXBeRGjgXP1Nkg+c+vKfVVV/P/fVAMAm0d1fqaqfSnJtvv/GhE9293cXtzIA2PDuSPLu7KC7301yR1X96WKWBKyGa0IDAAAAADDM+xa9AAAAAAAAti4RGgAAAACAYURoAAAAAACGEaEBAAAAABhGhAYAAAAAYJj/A4HttbpKQFqTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,4))\n",
    "df_train['label'].value_counts().plot(kind='bar')\n",
    "plt.axis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "7G5It2jFvF7B"
   },
   "outputs": [],
   "source": [
    "X_train, y_train = df_train['text'],df_train['label']\n",
    "X_test, y_test = df_test['text'],df_test['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pickle\n",
    "from emot.emo_unicode import *\n",
    "\n",
    "# 'Emoji_Dict.p'- download link https://drive.google.com/open?id=1G1vIkkbqPBYPKHcQ8qy0G2zkoab2Qv4v\n",
    "with open('Emoji_Dict.p', 'rb') as fp:\n",
    "    Emoji_Dict = pickle.load(fp)\n",
    "Emoji_Dict = {v: k for k, v in Emoji_Dict.items()}\n",
    "\n",
    "\n",
    "def convert_emojis_to_word(text):\n",
    "    for emot in Emoji_Dict:\n",
    "        text = re.sub(r'('+emot+')', \"_\".join(Emoji_Dict[emot].replace(\",\",\"\").replace(\":\",\"\").split()), text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3869\n"
     ]
    }
   ],
   "source": [
    "X_TRAIN=[]\n",
    "Y_TRAIN=[]\n",
    "\n",
    "for i in range(len(X_train)):\n",
    "    if(i==3869):\n",
    "        print(i)\n",
    "    else:\n",
    "        test=convert_emojis_to_word(X_train[df_train.index[i]])\n",
    "        X_TRAIN.append(test)\n",
    "        trainy= y_train[df_train.index[i]]\n",
    "        Y_TRAIN.append(trainy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5989"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_TRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5989"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_TRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'लामो प्रतीक्षा पछि आज रबि दाइलाइ सिधा कुरा कार्यक्रममा देख्दा खुसी लाग्यो । smiling_face_with_heart-eyessmiling_face_with_heart-eyessmiling_face_with_heart-eyessmiling_face_with_heart-eyes'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_TRAIN[3869]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'लामो प्रतीक्षा पछि आज रबि दाइलाइ सिधा कुरा कार्यक्रममा देख्दा खुसी लाग्यो । 😍😍😍😍'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_train[df_train.index[3870]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'लामो प्रतीक्षा पछि आज रबि दाइलाइ सिधा कुरा कार्यक्रममा देख्दा खुसी लाग्यो । smiling_face_with_heart-eyessmiling_face_with_heart-eyessmiling_face_with_heart-eyessmiling_face_with_heart-eyes'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert_emojis_to_word(X_train[df_train.index[3870]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "972\n"
     ]
    }
   ],
   "source": [
    "X_TEST=[]\n",
    "Y_TEST=[]\n",
    "\n",
    "\n",
    "for i in range(len(X_test)):\n",
    "  if(i==972):\n",
    "    print(i)\n",
    "  else:\n",
    "    test=convert_emojis_to_word(X_test[df_test.index[i]])\n",
    "    X_TEST.append(test)\n",
    "    testy= y_test[df_test.index[i]]\n",
    "    Y_TEST.append(testy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'कहिल्यै पनि राम्रो अभिभावक पायन अझै कति झुर हुनै बाकी छ'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_TRAIN[4484]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vSi0PinVS_Y5",
    "outputId": "c7dba299-a5bb-4682-986c-8557c4fa1528"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_TRAIN[4484]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UZYSpXNhS_mg",
    "outputId": "23348152-c2de-4387-b3b9-3888a7197845"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       असाध्यै राम्रो कार्यक्रम आयोजना गरिएको छ हजुरह...\n",
       "1       राम्रो कार्यक्रम, पहिलो सिजनले समेटेको कार्यक्...\n",
       "2       महानायक राजेश हमाल तपाई साँच्चै धन्यवादको पात्...\n",
       "3       जातको प्रष्न बाट सबै जनालाई सकरात्मक सन्देश मि...\n",
       "4       बहसको सुरुवात भएको छ, अझै जोडदार रुपमा गर्नुपर...\n",
       "                              ...                        \n",
       "1991          कस्ता कस्ता पागल memory king छन यार नेपालमा\n",
       "1992                            दोस्रो मूर्ख बिजय साही हो\n",
       "1993    बिजय शाहीलाई किन निरुत्साहित गरेको त पुण्य गौत...\n",
       "1994    यस्ता बिदेशी महादलालीहरु कहाँ गएर यो प्रश्नको ...\n",
       "1995     बुढा खालहरूमा बाँकि रहेको नेताहरुमा बाबुराम ज...\n",
       "Name: text, Length: 1994, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WwJvXnMsvF9Y",
    "outputId": "7f238ac2-d8f3-4687-ccfe-09ab5471af1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       बजार ले जसरी ट्रेन्ड चेन्ज गर्यो यो हेर्दा तत्...\n",
       "1       1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...\n",
       "2       होइन यो सानिमा बैंक ले bonus घोसणा गरेको २ महि...\n",
       "3       खैँ MBJC प्रति कित्तामा रू,10/-ले बढेर आज रू,1...\n",
       "4                                            राम्रो भयो️️\n",
       "                              ...                        \n",
       "5995    समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...\n",
       "5996    Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...\n",
       "5997    यस्तै यस्तै कार्यक्रम अझ बढी हुन जरुरी छ कुना ...\n",
       "5998                                   बधाई र सुभकामना ।।\n",
       "5999        कार्यक्रम एकदमै सकारात्मक छ। निरन्तरता पाओस्।\n",
       "Name: text, Length: 5991, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hK5y_pdF2Tix",
    "outputId": "af998f5d-7daf-4327-ba6f-cf175cf268f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/sumit/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zQBzZm5r58a2",
    "outputId": "9cceac7e-ad66-4dba-f9ed-d01679f3a8e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arabic\t     dutch    german\t  italian    portuguese  slovene  turkish\r\n",
      "azerbaijani  english  greek\t  kazakh     README\t spanish\r\n",
      "bengali      finnish  hungarian   nepali     romanian\t swedish\r\n",
      "danish\t     french   indonesian  norwegian  russian\t tajik\r\n"
     ]
    }
   ],
   "source": [
    "!ls /home/sumit/nltk_data/corpora/stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "GK2OhEQX8erE"
   },
   "outputs": [],
   "source": [
    "X_train=X_train.values.astype('U')\n",
    "X_test=X_test.values.astype('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pp_CUppn8k4r",
    "outputId": "be8360af-15d6-4fa1-8930-4c20a32d079f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       बजार ले जसरी ट्रेन्ड चेन्ज गर्यो यो हेर्दा तत्...\n",
       "1       1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...\n",
       "2       होइन यो सानिमा बैंक ले bonus घोसणा गरेको २ महि...\n",
       "3       खैँ MBJC प्रति कित्तामा रू,10/-ले बढेर आज रू,1...\n",
       "4                                            राम्रो भयो️️\n",
       "                              ...                        \n",
       "5995    समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...\n",
       "5996    Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...\n",
       "5997    यस्तै यस्तै कार्यक्रम अझ बढी हुन जरुरी छ कुना ...\n",
       "5998                                   बधाई र सुभकामना ।।\n",
       "5999        कार्यक्रम एकदमै सकारात्मक छ। निरन्तरता पाओस्।\n",
       "Name: text, Length: 5991, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_7rmG3b1vGAX",
    "outputId": "23b1ed06-2cf2-4793-c3e7-f0fadc68b2bd"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "#stopwords_list = stopwords.words('english') + stopwords.words('nepali')\n",
    "\n",
    "stopWords = set(nltk.corpus.stopwords.words('nepali'))\n",
    "\n",
    "cv_doc = CountVectorizer(max_features=20000,stop_words=stopWords ,ngram_range=(1,1), min_df=3,encoding='utf-8')\n",
    "cv_vector = cv_doc.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J97NiqwvgkNl",
    "outputId": "0d4e3872-eb25-4320-c8b5-60d8e6dc23a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-02-18 14:37:48--  https://raw.githubusercontent.com/sndsabin/Nepali-News-Classifier/master/nepali\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 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: 3613 (3.5K) [text/plain]\n",
      "Saving to: ‘nepali’\n",
      "\n",
      "\r",
      "nepali                0%[                    ]       0  --.-KB/s               \r",
      "nepali              100%[===================>]   3.53K  --.-KB/s    in 0s      \n",
      "\n",
      "2022-02-18 14:37:48 (37.1 MB/s) - ‘nepali’ saved [3613/3613]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://raw.githubusercontent.com/sndsabin/Nepali-News-Classifier/master/nepali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "L_Wkao4XNcos"
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "#from nltk import TweetTokenizer\n",
    "#tokenizer = TweetTokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zlg2jpCcgkC6",
    "outputId": "cf5e3a2b-1a09-45b6-ad76-cb383f9141e0"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopWords = nltk.corpus.stopwords.words('nepali') + stopwords.words('english') + nltk.corpus.stopwords.words('/home/sumit/Documents/nepali sentiment classifier/hindi')\n",
    "\n",
    "tfidfVectorizer = TfidfVectorizer(max_features=50000,tokenizer=lambda x: x.split(\" \") ,sublinear_tf=True, encoding='utf-8',decode_error='ignore',ngram_range=(1, 1),stop_words=stopWords)\n",
    "\n",
    "vectorised = tfidfVectorizer.fit(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "3B5_ddJOgj3I"
   },
   "outputs": [],
   "source": [
    "'''X_train = vectorised.transform(X_TRAIN)\n",
    "X_test= vectorised.transform(X_TEST)'''\n",
    "\n",
    "X_train = vectorised.transform(X_train)\n",
    "X_test= vectorised.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "id": "r9ysU6BdGDeI"
   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec, KeyedVectors\n",
    "#model = Word2Vec(X_train, size=1000, min_count=5, window=10, sg=0, workers=10)\n",
    "#print(\"Vocabulary dimension\", model.vector_size)\n",
    "#print(\"Number of words in vocab\", len(model.wv.vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "id": "FnR-6_OBGXMH"
   },
   "outputs": [],
   "source": [
    "#model.wv.most_similar('न', topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "k3ynYdINvGIv"
   },
   "outputs": [],
   "source": [
    "#X_train = cv_vector.transform(X_train)\n",
    "#X_test= cv_vector.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8I_TBxYUvGL_",
    "outputId": "82e5e713-e051-4ea7-943b-716cc1ccdb08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5990x15283 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 65424 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "HNGEgDiA8Ljg"
   },
   "outputs": [],
   "source": [
    "'''label_encoder = LabelEncoder().fit(Y_TRAIN)\n",
    "\n",
    "y_train = label_encoder.transform(Y_TRAIN)\n",
    "y_test = label_encoder.transform(Y_TEST)'''\n",
    "\n",
    "label_encoder = LabelEncoder().fit(y_train)\n",
    "\n",
    "y_train = label_encoder.transform(y_train)\n",
    "y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gaev856598LT",
    "outputId": "d9515a94-8efd-49d0-ab22-be84457c6e42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5885599598595083\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "clf=LinearSVC(loss='hinge',random_state=0,max_iter=10000,penalty='l2')\n",
    "clf.fit(X_train, y_train)    # training model on train data\n",
    "\n",
    "clfval = clf.predict(X_test)   # predicting test data\n",
    "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lit69RfI98IQ",
    "outputId": "980fca35-657f-4057-bc5e-ee51b35dd731"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5790265930757652\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(kernel='linear',random_state=0,max_iter=10000)\n",
    "clf.fit(X_train, y_train)    # training model on train data\n",
    "\n",
    "clfval = clf.predict(X_test)   # predicting test data\n",
    "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CvuVYiqGsgE3",
    "outputId": "26111401-e465-430a-9703-da41df5b810c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.5913654618473896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb=MultinomialNB(alpha=1.62,fit_prior=False)\n",
    "nb.fit(X_train,y_train)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "y_pred = nb.predict(X_test)\n",
    "\n",
    "print('accuracy %s' % f1_score(y_test, y_pred,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FAp6Gzs598Fx",
    "outputId": "88e38268-371d-4328-aecd-0e8907ef3130"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.562468640240843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sumit/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lrr=LogisticRegression(solver='saga', n_jobs=1, C=1e5)\n",
    "lrr.fit(X_train, y_train)\n",
    "lrrval = lrr.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, lrrval, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lse2rsYu98DD",
    "outputId": "b60da559-3d05-454f-836b-4a3d6357c1bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5664826894129453\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "clf = BernoulliNB(alpha=0.9,fit_prior=False)\n",
    "clf.fit(X_train, y_train)\n",
    "clfval = clf.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Bp0aJZwN98Ad",
    "outputId": "b9e696ba-ae94-46d8-b014-4d73c8b18ed3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5664826894129453\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import ComplementNB\n",
    "cllf = ComplementNB(alpha=1.0, fit_prior=False,norm=True)\n",
    "cllf.fit(X_train, y_train)\n",
    "cllfval = clf.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, cllfval, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-tu2ghVd9794",
    "outputId": "759206fa-9e9e-4dff-9b75-6f733449e6ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.564977420973407\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "cclf = PassiveAggressiveClassifier(max_iter=2500, random_state=43,tol=1e-5)\n",
    "cclf.fit(X_train, y_train)\n",
    "\n",
    "cllfval = cclf.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, cllfval, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C17lU2xO977L"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I9xpNFkyayA-",
    "outputId": "ccb3d928-8677-46e0-cb80-f77aec88b7b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5037631710988459\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.2,max_depth=10, random_state=0).fit(X_train, y_train)\n",
    "\n",
    "gbpred=clf.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, gbpred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_vqv5sTrax-F",
    "outputId": "6de805a3-7848-4b28-9c41-e4d566e245cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5363773206221776\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "percept = Perceptron(alpha=0.00001, tol=1e-6, penalty='l2', random_state=1, max_iter=3000)\n",
    "percept = percept.fit(X_train, y_train)\n",
    "\n",
    "cpfval = percept.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, cpfval, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "id": "shlLKSP3ax2B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score : 0.5730055193176117\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=150, random_state=2,max_features='sqrt',class_weight='balanced')\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "pred=clf.predict(X_test)\n",
    "print('F1 Score : {}'.format(f1_score(y_test, pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "id": "bfghz32IaxzY",
    "outputId": "335ea1d8-b7db-4cb5-ac08-56396edda3be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.76      0.59       609\n",
      "           1       0.72      0.73      0.72       887\n",
      "           2       0.49      0.14      0.22       496\n",
      "\n",
      "    accuracy                           0.59      1992\n",
      "   macro avg       0.56      0.54      0.51      1992\n",
      "weighted avg       0.59      0.59      0.56      1992\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "#countvector\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "fig, ax = plt.subplots(figsize=(15,10)) \n",
    "sns.heatmap(cf_matrix, linewidths=1, annot=True, ax=ax, fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling_strategy(y,n_samples, t='majority'):\n",
    "    target_classes = ''\n",
    "    if t == 'majority':\n",
    "        target_classes = {k:v for k,v in dict(Counter(y)).items() if v > n_samples}\n",
    "    elif t == 'minority':\n",
    "        # Since SMOTE depends on KNN, filtering out those samples that occur less than 10 times\n",
    "        target_classes = {k:v for k,v in dict(Counter(y)).items() if v < n_samples and v > 1236}\n",
    "    sampling_strategy = {k:n_samples for k,v in target_classes.items()}\n",
    "    return sampling_strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "over_sampler_ss = sampling_strategy(y_train,1000,t='minority')\n",
    "under_sampler_ss = sampling_strategy(y_train,8000,t='majority')\n",
    "over = SMOTE(sampling_strategy=over_sampler_ss)\n",
    "under = RandomUnderSampler(sampling_strategy=under_sampler_ss, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_over, y_over = over.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5989x15247 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 65146 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5989"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.5913654618473896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb=MultinomialNB(alpha=1.62,fit_prior=False)\n",
    "nb.fit(X_over,y_over)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "y_pred = nb.predict(X_test)\n",
    "\n",
    "print('accuracy %s' % f1_score(y_test, y_pred,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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