Emoji_Prediction.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.preprocessing.text import one_hot"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('Train.csv')\n"
      ],
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      "execution_count": 17,
      "outputs": []
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        "df.head()"
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      "execution_count": 18,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Unnamed: 0                                               TEXT  Label\n",
              "0           0  Vacation wasted ! #vacation2017 #photobomb #ti...      0\n",
              "1           1  Oh Wynwood, you’re so funny! : @user #Wynwood ...      1\n",
              "2           2  Been friends since 7th grade. Look at us now w...      2\n",
              "3           3  This is what it looks like when someone loves ...      3\n",
              "4           4  RT @user this white family was invited to a Bl...      3"
            ],
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              "      <td>Oh Wynwood, you’re so funny! : @user #Wynwood ...</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.iloc[:,1:]"
      ],
      "metadata": {
        "id": "jvo1_ualxpt6"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.head(25)"
      ],
      "metadata": {
        "id": "zXOhgxU4x2wv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['Label']"
      ],
      "metadata": {
        "id": "xjsy700lyobv"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.iloc[:,:1]"
      ],
      "metadata": {
        "id": "kHV6L9hky3iA"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=df['TEXT']"
      ],
      "metadata": {
        "id": "9YFh3hiwzCS8"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import nltk\n",
        "\n",
        "\n",
        "from nltk.stem import PorterStemmer as ps\n",
        "from nltk.stem import WordNetLemmatizer as wl\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "lammtizer=wl()\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "stemmer=ps()\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISXQTYTdzDC7",
        "outputId": "b893f6f5-1363-4ae7-8c59-a74fcc455fad"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "fin=[]\n",
        "for i in range(len(df)):\n",
        "  review=re.sub('[^a-zA-Z]',' ',x[i])\n",
        "  review=review.lower()\n",
        "  word=nltk.word_tokenize(review)\n",
        "  arr=[]\n",
        "  for j in word:\n",
        "    if j not in set(stopwords.words('english')):\n",
        "      arr.append(lammtizer.lemmatize(j))\n",
        "  review=' '.join(arr)\n",
        "  fin.append(review)"
      ],
      "metadata": {
        "id": "hRfmwpBYz2mO"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fin"
      ],
      "metadata": {
        "id": "uFLxpbcV04T3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=pd.DataFrame(fin)"
      ],
      "metadata": {
        "id": "gth0Rs3S4RTp"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "id": "qeihZY-K4hhV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "map=pd.read_csv('Mapping.csv')\n"
      ],
      "metadata": {
        "id": "UI_Eee123hc0"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "map=map.iloc[:,1:]\n",
        "map"
      ],
      "metadata": {
        "id": "7tnMbwp0630V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(0,70000):\n",
        "  i=0\n",
        "  for i in map['number']:\n",
        "    if(y[j]==map['number'][i]):\n",
        "      y[j]=map['emoticons'][i]"
      ],
      "metadata": {
        "id": "YlCvj9fT4lB0"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "2CcJHJpG5E9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c438cc36-88fd-41d4-9643-35806535c9a1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        😜\n",
              "1        📸\n",
              "2        😍\n",
              "3        😂\n",
              "4        😂\n",
              "        ..\n",
              "69995    😂\n",
              "69996    😎\n",
              "69997    😍\n",
              "69998    😂\n",
              "69999    💙\n",
              "Name: Label, Length: 70000, dtype: object"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file=open('/content/glove.6B.300d.txt','r',encoding='utf8')\n",
        "content=file.readlines()\n",
        "file.close()"
      ],
      "metadata": {
        "id": "UGwG_wTkOGbm"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content"
      ],
      "metadata": {
        "id": "UazJj-vwPBj7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeding={}\n",
        "\n",
        "\n",
        "for i in content:\n",
        "  i=i.split()\n",
        "  embeding[i[0]]=np.array(i[1:])"
      ],
      "metadata": {
        "id": "YRF02z2qPC5s"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeding"
      ],
      "metadata": {
        "id": "TPxFHg3zPiII"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer"
      ],
      "metadata": {
        "id": "F8RAoRJEv88H"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "onrhot=[one_hot(word,10000) for word in fin]"
      ],
      "metadata": {
        "id": "I6s5oyyH1jH2"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "onrhot"
      ],
      "metadata": {
        "id": "3F2G0cIWziN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_maxlen(fina):\n",
        "    maxlen = 0\n",
        "    for sent in fina:\n",
        "        maxlen = max(maxlen, len(sent))\n",
        "    return maxlen\n",
        "\n",
        "max_len = get_maxlen(fin)\n",
        "print(max_len)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSKcPr-U0QGy",
        "outputId": "12778e03-5a90-4ffb-876d-d6897d94789c"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "jWWj0TvX8pKG"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedded_docs=pad_sequences(onrhot,padding='post',maxlen=max_len)"
      ],
      "metadata": {
        "id": "XZWDDH9j8N9U"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedded_docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2can3EcI8pyb",
        "outputId": "723b9b6d-1205-4e9f-bb1d-4835eb4e163f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1010, 3657, 1010, ...,    0,    0,    0],\n",
              "       [3968, 3567, 8226, ...,    0,    0,    0],\n",
              "       [ 661, 1256, 8626, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [ 307, 7646, 5797, ...,    0,    0,    0],\n",
              "       [9423, 3065, 7748, ...,    0,    0,    0],\n",
              "       [5573,   29, 2938, ...,    0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "vQjSSmHOACtC"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Embedding(10001,300,input_length=max_len))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "model.compile(loss='CategoricalCrossentropy(from_logits=True)',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "fywdO7Yt8vyL"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgHp6edJA8Tm",
        "outputId": "ec4f2950-c0a7-4e06-ec0e-68e8db39ba87"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 127, 300)          3000300   \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 100)               160400    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,160,801\n",
            "Trainable params: 3,160,801\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zv9MEalxAoLM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}