Final_Twitter.ipynb

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      "source": [
        "**Loading and Intiating a variable with our Dataset**"
      ],
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            "text/plain": [
              "       label                                              tweet\n",
              "0          0      Sooo SAD I will miss you here in San Diego!!!\n",
              "1          0                          my boss is bullying me...\n",
              "2          0                     what interview! leave me alone\n",
              "3          0   Sons of ****, why couldn`t they put them on t...\n",
              "4          0       My Sharpie is running DANGERously low on ink\n",
              "...      ...                                                ...\n",
              "16358      1  morning twit-friends! welcome to my new followers\n",
              "16359      1  So I get up early and I feel good about the da...\n",
              "16360      1                                     enjoy ur night\n",
              "16361      1   Yay good for both of you. Enjoy the break - y...\n",
              "16362      1                         But it was worth it  ****.\n",
              "\n",
              "[16363 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>my boss is bullying me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>what interview! leave me alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>My Sharpie is running DANGERously low on ink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>16358</th>\n",
              "      <td>1</td>\n",
              "      <td>morning twit-friends! welcome to my new followers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16359</th>\n",
              "      <td>1</td>\n",
              "      <td>So I get up early and I feel good about the da...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16360</th>\n",
              "      <td>1</td>\n",
              "      <td>enjoy ur night</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16361</th>\n",
              "      <td>1</td>\n",
              "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16362</th>\n",
              "      <td>1</td>\n",
              "      <td>But it was worth it  ****.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv',encoding='latin-1')\n",
        "\n",
        "train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Counting the number of Postive and Negative Tweets from our CSV file Dataset**"
      ],
      "metadata": {
        "id": "0R9WzBSaqe6d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['label'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fGdaNQnkC_u",
        "outputId": "651158c7-bcfe-4a7c-e3f1-30713101bcab"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    8582\n",
              "0    7781\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now we will use glove lib which consists of pre-embdeded words so we dont have to embedded them.**"
      ],
      "metadata": {
        "id": "tyZaMGITqvH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "7afabbf7-7c0a-438d-973b-6ffeb3a4fa04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-11-18 13:15:31--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip         10%[=>                  ]  90.40M  5.00MB/s    eta 2m 5s  ^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip glove.6B.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWGHdpvTmqjY",
        "outputId": "c08cfade-9117-4313-afc5-39b66399c188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "words = dict()\n",
        "\n",
        "def add_to_dict(d, filename):\n",
        "  with open(filename, 'r') as f:\n",
        "    for line in f.readlines():\n",
        "      line = line.split(' ')\n",
        "\n",
        "      try:\n",
        "        d[line[0]] = np.array(line[1:], dtype=float)\n",
        "      except:\n",
        "        continue\n",
        "\n",
        "add_to_dict(words, 'glove.6B.50d.txt')\n",
        "words"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "bPh2bUHaetKU",
        "outputId": "051bd0ff-7b0c-4299-d609-4c9d22417454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-66dd8525e6af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0madd_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'glove.6B.50d.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-5-66dd8525e6af>\u001b[0m in \u001b[0;36madd_to_dict\u001b[0;34m(d, filename)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0madd_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m   \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m       \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'glove.6B.50d.txt'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(words)"
      ],
      "metadata": {
        "id": "5XbfgDWjfMvt",
        "outputId": "d91493cb-4861-4739-93d2-a2a46f5ac195",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "400000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now we will download some important and commonly used word or text libraries so we can use them later.**"
      ],
      "metadata": {
        "id": "6KXd4IXFrO_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "\n",
        "nltk.download('wordnet ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMJKe9Mpogu4",
        "outputId": "059da6d4-8dd0-440a-fe51-6a2630995d2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Error loading wordnet : Package 'wordnet ' not found in\n",
            "[nltk_data]     index\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('omw-1.4')\n",
        "nltk.download('wordnet')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JUyoYEqpvuB",
        "outputId": "edd66126-4296-484f-b064-dc9a71c228c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now we will user tokenizer so we can store our words from a sentence into word format in our array or list. As you can see below.**"
      ],
      "metadata": {
        "id": "Y2RMVKoUre9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "\n",
        "tokenizer.tokenize('@user when a father is dysfunctional and is')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "as_jyVF-ohw_",
        "outputId": "48b8dca2-98ba-4f30-b604-9f87c77ba273"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['user', 'when', 'a', 'father', 'is', 'dysfunctional', 'and', 'is']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We will aslo use word lemmatizer which basically does is convert our words **to the most commomly used word format you can see from the example below as **bold text**\n",
        "**'foot'  is the most commonly used word instead of feet so it will convert** **the word 'feet' to 'foot' so detection of our word sentiment becomes easier.**"
      ],
      "metadata": {
        "id": "mVwY4numr0pJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "#lemmatizer.lemmatize('feet')\n",
        "\n",
        "def message_to_token_list(s):\n",
        "  tokens = tokenizer.tokenize(s)\n",
        "  lowercased_tokens = [t.lower() for t in tokens]\n",
        "  lemmatized_tokens = [lemmatizer.lemmatize(t) for t in lowercased_tokens]\n",
        "  useful_tokens = [t for t in lemmatized_tokens if t in words]\n",
        "\n",
        "  return useful_tokens\n",
        "\n",
        "message_to_token_list('@user feet a fathers is dysfunctional and is')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwD_mViyotDH",
        "outputId": "f9cc4e2a-9249-4f11-be7a-ceca9ca5ad3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting Our words/sentences into vectors so we can train our model and so also we can use tokenizer and lemmatizer on them**"
      ],
      "metadata": {
        "id": "Ednbm4Z7s7Nc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def message_to_word_vectors(message, word_dict=words):\n",
        "  processed_list_of_tokens = message_to_token_list(message)\n",
        "\n",
        "  vectors = []\n",
        "\n",
        "  for token in processed_list_of_tokens:\n",
        "    if token not in word_dict:\n",
        "      continue\n",
        "    \n",
        "    token_vector = word_dict[token]\n",
        "    vectors.append(token_vector)\n",
        "  \n",
        "  return np.array(vectors, dtype=float)"
      ],
      "metadata": {
        "id": "s3LZRP8sqFhZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "message_to_word_vectors('@user when a father is dysfunctional and is').shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luBBpHC1qfvI",
        "outputId": "a5b1f885-2919-4499-e67f-4b65d6f371f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0,)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = train_df.sample(frac=1, random_state=1)\n",
        "train_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "split_index_1 = int(len(train_df) * 0.7)\n",
        "split_index_2 = int(len(train_df) * 0.85)\n",
        "\n",
        "train_df, val_df, test_df = train_df[:split_index_1], train_df[split_index_1:split_index_2], train_df[split_index_2:]\n",
        "\n",
        "len(train_df), len(val_df), len(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd5yVp05qh0Q",
        "outputId": "87aee861-4c65-483c-ba35-1f1d318f7e27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11454, 2454, 2455)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OaTLZpolqwDw",
        "outputId": "9d0a80c3-6ec6-4e49-bd0f-3215cf2494b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       label                                              tweet\n",
              "13908      0  Getting to play golf with our kickin` Leadersh...\n",
              "13909      0   so i feel like **** for saying HAPPY BELATED ...\n",
              "13910      0       Y AM I STILL AWAKE!!!! SMH.... THIS SUCKS!!!\n",
              "13911      1   HAHA I LOVE THE WAY U PUT IT 'He lifts our fe...\n",
              "13912      0            healthy food is NOT helping my hangover\n",
              "...      ...                                                ...\n",
              "16358      1  Happy Mothers Day. Take ur mothers into a spec...\n",
              "16359      0         is ur boyfriend better than mine? hell no!\n",
              "16360      1  I dedicated today to Mothers Day videos becaus...\n",
              "16361      0  i have no idea what im doing and i am complete...\n",
              "16362      1    mufasa!!!! warriors or the OCEAN!  hahahahahaha\n",
              "\n",
              "[2455 rows x 2 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <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>label</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13908</th>\n",
              "      <td>0</td>\n",
              "      <td>Getting to play golf with our kickin` Leadersh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13909</th>\n",
              "      <td>0</td>\n",
              "      <td>so i feel like **** for saying HAPPY BELATED ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13910</th>\n",
              "      <td>0</td>\n",
              "      <td>Y AM I STILL AWAKE!!!! SMH.... THIS SUCKS!!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13911</th>\n",
              "      <td>1</td>\n",
              "      <td>HAHA I LOVE THE WAY U PUT IT 'He lifts our fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13912</th>\n",
              "      <td>0</td>\n",
              "      <td>healthy food is NOT helping my hangover</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16358</th>\n",
              "      <td>1</td>\n",
              "      <td>Happy Mothers Day. Take ur mothers into a spec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16359</th>\n",
              "      <td>0</td>\n",
              "      <td>is ur boyfriend better than mine? hell no!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16360</th>\n",
              "      <td>1</td>\n",
              "      <td>I dedicated today to Mothers Day videos becaus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16361</th>\n",
              "      <td>0</td>\n",
              "      <td>i have no idea what im doing and i am complete...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16362</th>\n",
              "      <td>1</td>\n",
              "      <td>mufasa!!!! warriors or the OCEAN!  hahahahahaha</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2455 rows × 2 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ac5d8b06-4cd9-4caf-9235-42602d789d66 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ac5d8b06-4cd9-4caf-9235-42602d789d66');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_X_y(dff):\n",
        "  y = dff['label'].to_numpy().astype(int)\n",
        "\n",
        "  all_word_vector_sequences = []\n",
        "\n",
        "  for message in dff['tweet']:\n",
        "    message_as_vector_seq = message_to_word_vectors(message)\n",
        "    \n",
        "    if message_as_vector_seq.shape[0] == 0:\n",
        "      message_as_vector_seq = np.zeros(shape=(1, 50))\n",
        "\n",
        "    all_word_vector_sequences.append(message_as_vector_seq)\n",
        "  \n",
        "  return all_word_vector_sequences, y"
      ],
      "metadata": {
        "id": "CXWnsjkNqx9g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calculating average word size in a sentence or shape of the array or matrix so we can fix them and dont exceed it.**\n",
        "**After that we will split our dataset into train and test portions and then design our model and run the training.**"
      ],
      "metadata": {
        "id": "mnWGbrJyt1We"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = df_to_X_y(train_df)\n",
        "\n",
        "print(len(X_train), len(X_train[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c67vpBi1q4RR",
        "outputId": "0f9e65e9-c5b8-47e9-b84a-093435791d70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16363 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(X_train), len(X_train[2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWmsV6y_q6io",
        "outputId": "72dbf4d6-84c6-4b0f-d96b-791824c5c67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11454 26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequence_lengths = []\n",
        "\n",
        "for i in range(len(X_train)):\n",
        "  sequence_lengths.append(len(X_train[i]))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(sequence_lengths)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Kb2s6C0Dq8kA",
        "outputId": "32d9d553-bc46-4cd1-e28e-08e85e5267a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([    0.,     0.,     0.,     0.,     0., 16363.,     0.,     0.,\n",
              "            0.,     0.]),\n",
              " array([0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(sequence_lengths).describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9isJ9UjrB_B",
        "outputId": "43731a4b-5f20-4193-f415-0db41f6ea0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    11454.000000\n",
              "mean        13.446831\n",
              "std          6.945165\n",
              "min          1.000000\n",
              "25%          8.000000\n",
              "50%         13.000000\n",
              "75%         19.000000\n",
              "max         34.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from copy import deepcopy\n",
        "\n",
        "def pad_X(X, desired_sequence_length=57):\n",
        "  X_copy = deepcopy(X)\n",
        "\n",
        "  for i, x in enumerate(X):\n",
        "    x_seq_len = x.shape[0]\n",
        "    sequence_length_difference = desired_sequence_length - x_seq_len\n",
        "    \n",
        "    pad = np.zeros(shape=(sequence_length_difference, 50))\n",
        "\n",
        "    X_copy[i] = np.concatenate([x, pad])\n",
        "  \n",
        "  return np.array(X_copy).astype(float)"
      ],
      "metadata": {
        "id": "tlJHFoN0rDj5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pad_X(X_train)\n",
        "\n",
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkmvqHkirFVR",
        "outputId": "a4b27423-b8c0-4f1e-8db2-aa9a13a43e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11454, 57, 50)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5lWrux2rNoJ",
        "outputId": "c550bf9a-37bf-4deb-ebaf-cb1bbe940b3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11454,)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val, y_val = df_to_X_y(val_df)\n",
        "X_val = pad_X(X_val)\n",
        "\n",
        "X_val.shape, y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-It0fxJrQZg",
        "outputId": "9c532f59-8d82-4dfb-8ca2-07456928fefb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2454, 57, 50), (2454,))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, y_test = df_to_X_y(test_df)\n",
        "X_test = pad_X(X_test)\n",
        "\n",
        "X_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UveVGX2PrTeC",
        "outputId": "e464045b-ffdd-4686-b39b-4c42743169f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2455, 57, 50), (2455,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Our Model using LSTM\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "model = Sequential([])\n",
        "\n",
        "model.add(layers.Input(shape=(57, 50)))\n",
        "model.add(layers.LSTM(64, return_sequences=True))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.LSTM(64, return_sequences=True))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.LSTM(64, return_sequences=True))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "lZFlpb9BrVQL"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AMdC6NTzrsAq",
        "outputId": "41ec0d45-55e4-4aaa-e364-2561d22b475d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 57, 64)            29440     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 57, 64)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 57, 64)            33024     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 57, 64)            0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 57, 64)            33024     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 57, 64)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 3648)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 3649      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 99,137\n",
            "Trainable params: 99,137\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving our Model\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.metrics import AUC\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "cp = ModelCheckpoint('/content/drive/MyDrive/models/tw.h', save_best_only=True)\n",
        "#Hyper-parameters of our model\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001), \n",
        "              loss=BinaryCrossentropy(), \n",
        "              metrics=['accuracy', AUC(name='auc')])"
      ],
      "metadata": {
        "id": "niOD1860rteJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "tTDln5agrv1h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequencies = pd.value_counts(train_df['label'])\n",
        "\n",
        "frequencies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hFX_at7uYBy",
        "outputId": "0debca3b-92c3-4d50-d8fa-b0333eb2fc89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    6031\n",
              "0    5423\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights = {0: frequencies.sum() / frequencies[0], 1: frequencies.sum() / frequencies[1]}\n",
        "weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8GlmVEJuaMs",
        "outputId": "623a0db8-b004-4574-ed69-6b4ef2cc08a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 2.1121150654619214, 1: 1.8991875310893716}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitiing our training our model\n",
        "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=20, callbacks=[cp], class_weight=weights)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFtr1ad2ucaD",
        "outputId": "aa23fb7c-44d6-43f4-bcc6-d0425125e78e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "357/358 [============================>.] - ETA: 0s - loss: 1.1620 - accuracy: 0.6896 - auc: 0.7629"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 32s 60ms/step - loss: 1.1613 - accuracy: 0.6899 - auc: 0.7632 - val_loss: 0.5223 - val_accuracy: 0.7535 - val_auc: 0.8302\n",
            "Epoch 2/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.9732 - accuracy: 0.7695 - auc: 0.8464"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 17s 49ms/step - loss: 0.9724 - accuracy: 0.7694 - auc: 0.8467 - val_loss: 0.4800 - val_accuracy: 0.7718 - val_auc: 0.8549\n",
            "Epoch 3/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.9248 - accuracy: 0.7858 - auc: 0.8632"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 20s 56ms/step - loss: 0.9246 - accuracy: 0.7858 - auc: 0.8633 - val_loss: 0.4587 - val_accuracy: 0.7848 - val_auc: 0.8653\n",
            "Epoch 4/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.8931 - accuracy: 0.7927 - auc: 0.8731"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 49ms/step - loss: 0.8940 - accuracy: 0.7924 - auc: 0.8729 - val_loss: 0.4475 - val_accuracy: 0.7930 - val_auc: 0.8730\n",
            "Epoch 5/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.8704 - accuracy: 0.7982 - auc: 0.8800"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 49ms/step - loss: 0.8695 - accuracy: 0.7983 - auc: 0.8802 - val_loss: 0.4387 - val_accuracy: 0.7991 - val_auc: 0.8794\n",
            "Epoch 6/20\n",
            "358/358 [==============================] - ETA: 0s - loss: 0.8546 - accuracy: 0.8011 - auc: 0.8848"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 51ms/step - loss: 0.8546 - accuracy: 0.8011 - auc: 0.8848 - val_loss: 0.4270 - val_accuracy: 0.8048 - val_auc: 0.8849\n",
            "Epoch 7/20\n",
            "358/358 [==============================] - ETA: 0s - loss: 0.8401 - accuracy: 0.8049 - auc: 0.8887"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 50ms/step - loss: 0.8401 - accuracy: 0.8049 - auc: 0.8887 - val_loss: 0.4205 - val_accuracy: 0.8097 - val_auc: 0.8885\n",
            "Epoch 8/20\n",
            "357/358 [============================>.] - ETA: 0s - loss: 0.8121 - accuracy: 0.8166 - auc: 0.8964"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 17s 49ms/step - loss: 0.8125 - accuracy: 0.8166 - auc: 0.8963 - val_loss: 0.4147 - val_accuracy: 0.8142 - val_auc: 0.8932\n",
            "Epoch 9/20\n",
            "357/358 [============================>.] - ETA: 0s - loss: 0.7998 - accuracy: 0.8204 - auc: 0.8993"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 19s 53ms/step - loss: 0.7993 - accuracy: 0.8204 - auc: 0.8995 - val_loss: 0.4126 - val_accuracy: 0.8183 - val_auc: 0.8959\n",
            "Epoch 10/20\n",
            "357/358 [============================>.] - ETA: 0s - loss: 0.7792 - accuracy: 0.8269 - auc: 0.9053"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 17s 47ms/step - loss: 0.7801 - accuracy: 0.8266 - auc: 0.9050 - val_loss: 0.4052 - val_accuracy: 0.8248 - val_auc: 0.8997\n",
            "Epoch 11/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.7579 - accuracy: 0.8340 - auc: 0.9103"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 50ms/step - loss: 0.7584 - accuracy: 0.8339 - auc: 0.9102 - val_loss: 0.3952 - val_accuracy: 0.8236 - val_auc: 0.9025\n",
            "Epoch 12/20\n",
            "358/358 [==============================] - 6s 18ms/step - loss: 0.7456 - accuracy: 0.8338 - auc: 0.9132 - val_loss: 0.3953 - val_accuracy: 0.8280 - val_auc: 0.9055\n",
            "Epoch 13/20\n",
            "358/358 [==============================] - ETA: 0s - loss: 0.7270 - accuracy: 0.8407 - auc: 0.9175"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 49ms/step - loss: 0.7270 - accuracy: 0.8407 - auc: 0.9175 - val_loss: 0.3858 - val_accuracy: 0.8301 - val_auc: 0.9078\n",
            "Epoch 14/20\n",
            "358/358 [==============================] - 5s 15ms/step - loss: 0.7119 - accuracy: 0.8456 - auc: 0.9210 - val_loss: 0.3962 - val_accuracy: 0.8321 - val_auc: 0.9103\n",
            "Epoch 15/20\n",
            "358/358 [==============================] - ETA: 0s - loss: 0.7070 - accuracy: 0.8476 - auc: 0.9221"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 49ms/step - loss: 0.7070 - accuracy: 0.8476 - auc: 0.9221 - val_loss: 0.3826 - val_accuracy: 0.8337 - val_auc: 0.9114\n",
            "Epoch 16/20\n",
            "356/358 [============================>.] - ETA: 0s - loss: 0.7040 - accuracy: 0.8473 - auc: 0.9229"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 19s 54ms/step - loss: 0.7029 - accuracy: 0.8479 - auc: 0.9232 - val_loss: 0.3801 - val_accuracy: 0.8362 - val_auc: 0.9113\n",
            "Epoch 17/20\n",
            "355/358 [============================>.] - ETA: 0s - loss: 0.6788 - accuracy: 0.8569 - auc: 0.9282"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 18s 50ms/step - loss: 0.6798 - accuracy: 0.8567 - auc: 0.9280 - val_loss: 0.3758 - val_accuracy: 0.8358 - val_auc: 0.9145\n",
            "Epoch 18/20\n",
            "358/358 [==============================] - 5s 15ms/step - loss: 0.6708 - accuracy: 0.8577 - auc: 0.9300 - val_loss: 0.3770 - val_accuracy: 0.8370 - val_auc: 0.9161\n",
            "Epoch 19/20\n",
            "358/358 [==============================] - ETA: 0s - loss: 0.6560 - accuracy: 0.8624 - auc: 0.9332"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r358/358 [==============================] - 17s 49ms/step - loss: 0.6560 - accuracy: 0.8624 - auc: 0.9332 - val_loss: 0.3691 - val_accuracy: 0.8411 - val_auc: 0.9158\n",
            "Epoch 20/20\n",
            "358/358 [==============================] - 6s 18ms/step - loss: 0.6430 - accuracy: 0.8668 - auc: 0.9357 - val_loss: 0.3737 - val_accuracy: 0.8403 - val_auc: 0.9177\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f94ca594c10>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading our model for prediction of our tweets.**"
      ],
      "metadata": {
        "id": "SgxEV_15u8Ir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "best_model = load_model('/content/drive/MyDrive/models/tw.h')"
      ],
      "metadata": {
        "id": "4vo-35QP24Te"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding out loss and accuracy of our model.**"
      ],
      "metadata": {
        "id": "N_pM26kz3tyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = (best_model.predict(X_test) > 0.5).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, test_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gD_SbkYM3FFW",
        "outputId": "d7f9d098-7a08-4d07-c57e-b2696638d688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "77/77 [==============================] - 1s 6ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.87      0.84      1147\n",
            "           1       0.88      0.81      0.85      1308\n",
            "\n",
            "    accuracy                           0.84      2455\n",
            "   macro avg       0.84      0.84      0.84      2455\n",
            "weighted avg       0.84      0.84      0.84      2455\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Give our model an example to predict the output.**"
      ],
      "metadata": {
        "id": "tCKMYKDu4IHR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a='the food was good not bad at all '\n",
        "b=message_to_token_list(a)\n",
        "print(b)\n",
        "c=message_to_word_vectors(a, word_dict=words)\n",
        "print(c)\n",
        "l=1\n",
        "#y = d.to_numpy().astype(int)\n",
        "\n",
        "all_word_vector_sequences = []\n",
        "\n",
        "if c.shape[0] == 0:\n",
        "  c = np.zeros(shape=(1, 50))\n",
        "  \n",
        "all_word_vector_sequences.append(c)\n",
        "\n",
        "d=pad_X(all_word_vector_sequences, desired_sequence_length=57)\n",
        "\n",
        "print(best_model.predict(d)) \n",
        "\n",
        "t_predictions = (best_model.predict(d) > 0.5).astype(int)\n",
        "\n",
        "\n",
        "print('The Sentiment of this tweet is: ',t_predictions)"
      ],
      "metadata": {
        "id": "Nx07GEDvyEO2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cfc2229-02d1-404d-ec43-f8cc8e2e5845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[]\n",
            "[]\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "[[0.49334735]]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "The Sentiment of this tweet is:  [[0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Use this function if you want remove unneeded symbols\n",
        "import re\n",
        "\n",
        "def text_cleaning(text):\n",
        "  text = re.sub(r'@[A-Za-z0-9]+', '', text)     # removing @mentions\n",
        "  text = re.sub(r'@[A-Za-zA-Z0-9]+', '', text)  # removing @mentions \n",
        "  text = re.sub(r'@[A-Za-z]+', '', text)        # removing @mentions\n",
        "  text = re.sub(r'@[-)]+', '', text)            # removing @mentions\n",
        "  text = re.sub(r'#', '', text )                # removing '#' sign\n",
        "  text = re.sub(r'RT[\\s]+', '', text)           # removing RT\n",
        "  text = re.sub(r'https?\\/\\/\\S+', '', text)     # removing the hyper link\n",
        "  text = re.sub(r'&[a-z;]+', '', text)          # removing '&gt;'\n",
        "\n",
        "  return text\n",
        "\n",
        "# applying the text cleaning function on tweets\n",
        "df['SentimentText'] = df['SentimentText'].apply(text_cleaning)\n",
        "df.head(10)\n"
      ],
      "metadata": {
        "id": "h-bwJEh5oOlS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "f204d741-fdcc-479b-bc91-cdc1758847d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-11b4228e9a44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# applying the text cleaning function on tweets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SentimentText'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SentimentText'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_cleaning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ahdqCUsenDM6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}