Modeltraining_testing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "finalsentiment.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNbt2zbLfO835r7QczXk2PZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/siddharth1704/Twitter_sentiment/blob/main/finalsentiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6G6x-mZobjvC"
      },
      "source": [
        "import pandas as pd\n",
        "import os"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCRrUX-sbsQ7"
      },
      "source": [
        "import numpy as np\n",
        "from numpy import array"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMpNU69lb1Wb",
        "outputId": "cc89650e-a2bb-40b0-a516-0aa38a042bb3",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files \n",
        "  \n",
        "  \n",
        "uploaded = files.upload()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-467f6e64-e915-4259-ad1c-2ea7bd24f25d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-467f6e64-e915-4259-ad1c-2ea7bd24f25d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving file2.csv to file2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Lt0zPqGdnjx",
        "outputId": "d85672c2-44d2-45d3-ef65-bfe6d1def371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "\n",
        "import pandas as pd \n",
        "import io \n",
        "  \n",
        "df = pd.read_csv(io.BytesIO(uploaded['file2.csv'])) \n",
        "print(df)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Unnamed: 0                                             Tweets  sentiment\n",
            "0             0  RT @AlistairHaimes: Of the whole omnishambles,...          1\n",
            "1             1  RT @SteveBakerHW: The case for a new coronavir...          0\n",
            "2             2  RT @atrupar: \"If we tested half, cases would b...          1\n",
            "3             3  @Jim_Jordan Wear a mask! 224,000+ American sou...          0\n",
            "4             4  RT @BNODesk: The El Paso-Juárez area on the U....          0\n",
            "..          ...                                                ...        ...\n",
            "194         194  RT @DonaldJTrumpJr: Muslim Group Says Cuomo Is...          0\n",
            "195         195  RT @HKLiberty_Team: #HKSARG relaxes max partic...          1\n",
            "196         196  RT @AmyMcGrathKY: Sen. McConnell blocked $1,20...          1\n",
            "197         197  RT @SteveBakerHW: The case for a new coronavir...          1\n",
            "198         198  RT @nytimes: More than 75,000 coronavirus case...          1\n",
            "\n",
            "[199 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6GNPaCBgC5e"
      },
      "source": [
        "import re\n",
        "#cleaning the data\n",
        "def cleanTxt(text):\n",
        "    text=re.sub(r'@[A-Za-z0-9,.'']+','',text) #remove @mentions\n",
        "    text=re.sub(r'#','',text) #Removing the # symbol\n",
        "    text=re.sub(r'https?:\\/\\/\\S+','',text)#remove hyperlink\n",
        "    text=re.sub(r'RT[\\s]+','',text)#removing RT\n",
        "    \n",
        "    return text\n",
        "#cleaning the new data\n",
        "df['Tweets']=df['Tweets'].apply(cleanTxt)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CciuOnHldzgd"
      },
      "source": [
        "\n",
        "import spacy\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "import string\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K52aXt7Ud6lg"
      },
      "source": [
        "stop_words = list(STOP_WORDS)\n",
        "pun = string.punctuation"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rA9tzxn4eBdA"
      },
      "source": [
        "#removing the stopwords\n",
        "def data_cleaning(sentence):\n",
        "    doc = nlp(sentence)\n",
        "    \n",
        "    tokens = []\n",
        "    for token in doc:\n",
        "        if token.lemma_ != \"-PRON-\":\n",
        "            temp = token.lemma_.lower().strip()\n",
        "        else:\n",
        "            temp = token.lower_\n",
        "        tokens.append(temp)\n",
        "        \n",
        "    clean_tokens = []\n",
        "    for token in tokens:\n",
        "        if token not in stop_words and token not in pun:\n",
        "            clean_tokens.append(token)\n",
        "    return clean_tokens\n",
        "# Function to convert \n",
        "def listToString(s): \n",
        "    # initialize an empty string \n",
        "    str1 = \" \"        # return string \n",
        "    return (str1.join(s))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-AsZ_NmgAgD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saJ1fboCeJV1"
      },
      "source": [
        "ctweets = []\n",
        "tweets = list(df['Tweets'])\n",
        "for Tweet in tweets:\n",
        "    ctweets.append(listToString(data_cleaning(Tweet)))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOHpcEc0fFxr",
        "outputId": "8da397fe-6e0f-44bc-a837-4099714ee914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ctweets[3]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'jordan wear mask 224,000 american soul lose covid19 true patriot wear mask th …'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uxg5bv76fRUt"
      },
      "source": [
        "sentiments = df['sentiment']\n",
        "\n",
        "from numpy import array\n",
        "sentiments = np.array(list(sentiments))"
      ],
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLwzzMdth3KV"
      },
      "source": [
        "from keras.preprocessing.text import one_hot\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Activation, Dropout, Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.embeddings import Embedding\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "ctweets_train, ctweets_test, sentiments_train, sentiments_test = train_test_split(ctweets, sentiments, test_size = 0.20, random_state = 42)"
      ],
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "st_aXiYCiWH6"
      },
      "source": [
        "\n",
        "tokenizer = Tokenizer(num_words = 5000)\n",
        "tokenizer.fit_on_texts(ctweets_train)\n",
        "\n",
        "ctweets_train = tokenizer.texts_to_sequences(ctweets_train)\n",
        "ctweets_test = tokenizer.texts_to_sequences(ctweets_test)"
      ],
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RL3dM2Yoiq3H"
      },
      "source": [
        "\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "maxlen = 5000\n",
        "\n",
        "ctweets_train = pad_sequences(ctweets_train, padding='post', maxlen = maxlen)\n",
        "ctweets_test = pad_sequences(ctweets_test, padding='post', maxlen = maxlen)\n"
      ],
      "execution_count": 327,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SF69F0zbpuM6",
        "outputId": "179d1583-cf71-482b-d09c-1c0da02a0641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "glove.6B.zip.1       59%[==========>         ] 487.38M  2.23MB/s    eta 2m 35s ^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNdFtlOSsFdn"
      },
      "source": [
        "!unzip glove*.zip"
      ],
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bu7zcxWasNor",
        "outputId": "6425f32a-a70c-4051-c5a6-dda834f4b43d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "!ls\n",
        "!pwd"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "file2.csv\t   glove.6B.200d.txt  glove.6B.50d.txt\tsample_data\n",
            "glove.6B.100d.txt  glove.6B.300d.txt  glove.6B.zip\n",
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UbG-WcNizam"
      },
      "source": [
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "\n",
        "embeddings_dict = dict()\n",
        "glove = open('glove.6B.100d.txt', encoding=\"utf8\")\n",
        "\n",
        "for line in glove:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = asarray(records[1:],dtype = 'float32')\n",
        "    embeddings_dict [word] = vector_dimensions\n",
        "glove.close()"
      ],
      "execution_count": 339,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HQb2048sa3h"
      },
      "source": [
        "embedding_matrix = zeros((vocab_size,100))\n",
        "for word, index in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_dict.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector"
      ],
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLGeoSdHsq8Z"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from keras.layers import Input, Dense, Embedding, SpatialDropout1D, add, concatenate,LSTM,Conv1D\n",
        "from keras.layers import  Bidirectional, GlobalMaxPooling1D, GlobalAveragePooling1D\n",
        "#model = Sequential()\n",
        "#embedding_layer = Embedding(vocab_size, 100, weights = [embedding_matrix], input_length = maxlen, trainable = False)\n",
        "#model.add(embedding_layer)\n",
        "#model.add(SpatialDropout1D(0.3))\n",
        "#model.add(Flatten())\n",
        "#model.add(Activation('softmax'))\n",
        "#model.add(Dense(6, activation = 'relu'))\n",
        "#model.add(Dense(1, activation = 'sigmoid'))\n",
        "#model.add(layers.Dense(64, activation='relu'))\n",
        "#model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal',activation='selu'))\n",
        "\n"
      ],
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZD_kVgESssrD"
      },
      "source": [
        "\n",
        "#optimizer = keras.optimizers.SGD(learning_rate=lr_schedule)\n",
        "#model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics=['acc'])\n",
        "#print(model.summary())"
      ],
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtLEWvq2TCCZ",
        "outputId": "ec993205-0f8c-4fa6-dc0e-567d2a762ebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, 100),\n",
        "   \n",
        "    tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_88\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_88 (Embedding)     (None, None, 100)         67800     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_28 (Glo (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_138 (Dense)            (None, 24)                2424      \n",
            "_________________________________________________________________\n",
            "dense_139 (Dense)            (None, 1)                 25        \n",
            "=================================================================\n",
            "Total params: 70,249\n",
            "Trainable params: 70,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODyjpUpvTY7D",
        "outputId": "8bd6a3b8-7065-46c9-d58f-e88d66089f7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        }
      },
      "source": [
        "history = model.fit(ctweets_test, sentiments_test, epochs=20, validation_data=(ctweets_test, sentiments_test), verbose=1)"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "2/2 [==============================] - 0s 87ms/step - loss: 0.6913 - accuracy: 0.6000 - val_loss: 0.6848 - val_accuracy: 0.6000\n",
            "Epoch 2/20\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.6845 - accuracy: 0.6000 - val_loss: 0.6790 - val_accuracy: 0.6000\n",
            "Epoch 3/20\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 0.6783 - accuracy: 0.6000 - val_loss: 0.6729 - val_accuracy: 0.6000\n",
            "Epoch 4/20\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 0.6722 - accuracy: 0.6000 - val_loss: 0.6668 - val_accuracy: 0.6000\n",
            "Epoch 5/20\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 0.6664 - accuracy: 0.6000 - val_loss: 0.6606 - val_accuracy: 0.6000\n",
            "Epoch 6/20\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 0.6600 - accuracy: 0.6000 - val_loss: 0.6542 - val_accuracy: 0.6000\n",
            "Epoch 7/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.6534 - accuracy: 0.6000 - val_loss: 0.6478 - val_accuracy: 0.6000\n",
            "Epoch 8/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.6471 - accuracy: 0.6000 - val_loss: 0.6413 - val_accuracy: 0.6000\n",
            "Epoch 9/20\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.6408 - accuracy: 0.6000 - val_loss: 0.6344 - val_accuracy: 0.6250\n",
            "Epoch 10/20\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.6336 - accuracy: 0.6250 - val_loss: 0.6269 - val_accuracy: 0.6250\n",
            "Epoch 11/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.6261 - accuracy: 0.6250 - val_loss: 0.6191 - val_accuracy: 0.6500\n",
            "Epoch 12/20\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.6181 - accuracy: 0.6500 - val_loss: 0.6107 - val_accuracy: 0.7000\n",
            "Epoch 13/20\n",
            "2/2 [==============================] - 0s 41ms/step - loss: 0.6101 - accuracy: 0.7000 - val_loss: 0.6019 - val_accuracy: 0.7000\n",
            "Epoch 14/20\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.6009 - accuracy: 0.7000 - val_loss: 0.5925 - val_accuracy: 0.7250\n",
            "Epoch 15/20\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.5914 - accuracy: 0.7500 - val_loss: 0.5826 - val_accuracy: 0.8000\n",
            "Epoch 16/20\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.5817 - accuracy: 0.8000 - val_loss: 0.5724 - val_accuracy: 0.8000\n",
            "Epoch 17/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.5715 - accuracy: 0.8250 - val_loss: 0.5616 - val_accuracy: 0.8750\n",
            "Epoch 18/20\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.5606 - accuracy: 0.8750 - val_loss: 0.5502 - val_accuracy: 0.8750\n",
            "Epoch 19/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.5491 - accuracy: 0.8750 - val_loss: 0.5381 - val_accuracy: 0.9250\n",
            "Epoch 20/20\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.5374 - accuracy: 0.9250 - val_loss: 0.5256 - val_accuracy: 0.9250\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}