EMOJI_PREDICTION.ipynb

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        "id": "YjkzSknb-ubu"
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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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('Train.csv')"
      ],
      "metadata": {
        "id": "WOvupOuT_KHn"
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      "execution_count": 2,
      "outputs": []
    },
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        "df.head()"
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              "   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",
              "      <td>1</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.iloc[:,1:]"
      ],
      "metadata": {
        "id": "x60YYqwh_UUH"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=df['TEXT']"
      ],
      "metadata": {
        "id": "Zx3gDYIp_dvd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['Label']"
      ],
      "metadata": {
        "id": "BOphCn-vBia5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTC-C-fnBnqP",
        "outputId": "5446641d-9e2b-4e96-d9c5-d936cc6b5113"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         0\n",
              "1         1\n",
              "2         2\n",
              "3         3\n",
              "4         3\n",
              "         ..\n",
              "69995     3\n",
              "69996    16\n",
              "69997     2\n",
              "69998     3\n",
              "69999    14\n",
              "Name: Label, Length: 70000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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')\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCcv0e0R_icv",
        "outputId": "27a23777-6af2-4e0e-aa14-8f09207f34e1"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "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)\n",
        ""
      ],
      "metadata": {
        "id": "XNxMHJEZ_2CR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fin"
      ],
      "metadata": {
        "id": "Ab4G277d_45_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import one_hot\n",
        "onrhot=[one_hot(word,520000) for word in fin]"
      ],
      "metadata": {
        "id": "OJ_vzrrXAfcV"
      },
      "execution_count": 41,
      "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": "6-8ZvrLqAz5r",
        "outputId": "a7897143-b9c8-4e55-e832-faf32a285430"
      },
      "execution_count": 12,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=[]\n",
        "def get_words(fina):\n",
        "  for sen in fina:\n",
        "    word=nltk.word_tokenize(sen)\n",
        "    for j in word:\n",
        "      a.append(j)"
      ],
      "metadata": {
        "id": "51DQXY-aTbRK"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_words(fin)"
      ],
      "metadata": {
        "id": "3JmIkPa4UZM9"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2UZC0QpUmCK",
        "outputId": "591faf59-d75c-4b59-ff40-880fad43f0a6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "514629"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "docs=pad_sequences(onrhot,padding='post',maxlen=max_len)"
      ],
      "metadata": {
        "id": "CgG7ItggA6Br"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NW1ZWmnBFlj",
        "outputId": "e6df8686-4b48-43e4-9f05-fef08975ca46"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 31289,  26552,  31289, ...,      0,      0,      0],\n",
              "       [321798, 310146, 255554, ...,      0,      0,      0],\n",
              "       [423048, 462099, 137029, ...,      0,      0,      0],\n",
              "       ...,\n",
              "       [171410, 344672, 191413, ...,      0,      0,      0],\n",
              "       [190560, 286519, 345689, ...,      0,      0,      0],\n",
              "       [238059, 270646,   2610, ...,      0,      0,      0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "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": "gD3k_wfIBHLa"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Embedding(input_dim = 520000,\n",
        "              output_dim = 100,\n",
        "              input_length = max_len,\n",
        "             ),\n",
        "    LSTM(units = 100),\n",
        "    Dense(20, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "B-kY0mgBBWuV"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBzooI15azjB",
        "outputId": "d4e547ae-571b-4ee2-bf3b-b2ab84e2659b"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_10 (Embedding)    (None, 127, 100)          52000000  \n",
            "                                                                 \n",
            " lstm_12 (LSTM)              (None, 100)               80400     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 20)                2020      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 52,082,420\n",
            "Trainable params: 52,082,420\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=np.array(docs)"
      ],
      "metadata": {
        "id": "xSXitLi4B3UJ"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmdBteumWKvJ",
        "outputId": "8815f777-007d-41a0-ae1e-1f7afa4eebce"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 31289,  26552,  31289, ...,      0,      0,      0],\n",
              "       [321798, 310146, 255554, ...,      0,      0,      0],\n",
              "       [423048, 462099, 137029, ...,      0,      0,      0],\n",
              "       ...,\n",
              "       [171410, 344672, 191413, ...,      0,      0,      0],\n",
              "       [190560, 286519, 345689, ...,      0,      0,      0],\n",
              "       [238059, 270646,   2610, ...,      0,      0,      0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_tarin=np.array(y)\n",
        "y_tarin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rqPDx0gCLbd",
        "outputId": "d88e37af-a5cc-4ca5-c624-9bd1d16e0b27"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2, ...,  2,  3, 14])"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_tarin.shape,x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNmdzFx-DFqF",
        "outputId": "1efb3e15-f42b-4b17-fb5b-2176053e974f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70000,), (70000, 127))"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aaZ7EGqfbPHR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}