90. Twitter Tweet Emotion Detection.ipynb

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        "outputId": "38003209-7d87-48a2-db15-e0212f502f07"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading emotion-prediction-with-semi-supervised-learning.zip to /content\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0.00/2.22M [00:00<?, ?B/s]\r 45%|████▍     | 1.00M/2.22M [00:00<00:00, 1.47MB/s]\r 90%|████████▉ | 2.00M/2.22M [00:00<00:00, 2.89MB/s]\r100%|██████████| 2.22M/2.22M [00:00<00:00, 2.75MB/s]\n"
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      ],
      "source": [
        "%%bash\n",
        "\n",
        "pip install -q kaggle\n",
        "mkdir -p ~/.kaggle\n",
        "cp drive/MyDrive/kaggle.json ~/.kaggle/\n",
        "chmod 600 /root/.kaggle/kaggle.json\n",
        "kaggle datasets download -d emirhanai/emotion-prediction-with-semi-supervised-learning\n",
        "unzip -qq emotion-prediction-with-semi-supervised-learning.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Emotion Prediction with Semi Supervised Learning"
      ],
      "metadata": {
        "id": "y16frwKLZTq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
        "\n",
        "\n",
        "df = pd.read_csv('/content/tweet_emotions.csv')\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sKaQSD2gkOUT",
        "outputId": "f8427019-ba96-4553-90f1-61ff8466b231"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id   sentiment                                            content\n",
              "0  1956967341       empty  @tiffanylue i know  i was listenin to bad habi...\n",
              "1  1956967666     sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2  1956967696     sadness                Funeral ceremony...gloomy friday...\n",
              "3  1956967789  enthusiasm               wants to hang out with friends SOON!\n",
              "4  1956968416     neutral  @dannycastillo We want to trade with someone w..."
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>content</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1956967341</td>\n",
              "      <td>empty</td>\n",
              "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1956967666</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1956967696</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1956967789</td>\n",
              "      <td>enthusiasm</td>\n",
              "      <td>wants to hang out with friends SOON!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1956968416</td>\n",
              "      <td>neutral</td>\n",
              "      <td>@dannycastillo We want to trade with someone w...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import RidgeClassifier\n",
        "from sklearn.semi_supervised import SelfTrainingClassifier\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df.content, df.sentiment,\n",
        "                                                        test_size=0.0007000000000000001,\n",
        "                                                        random_state=1,shuffle=True)\n",
        "    \n",
        "X_CountVectorizer = CountVectorizer(stop_words='english')\n",
        "X_train_counts = X_CountVectorizer.fit_transform(X_train)\n",
        "X_TfidfTransformer = TfidfTransformer()\n",
        "X_train_tfidf = X_TfidfTransformer.fit_transform(X_train_counts)\n",
        "\n",
        "clf = SelfTrainingClassifier(RidgeClassifier())\n",
        "clf.fit(X_train_tfidf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dojl7A6dkR-w",
        "outputId": "914b239e-a74f-4678-fda1-95f247501d37"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/semi_supervised/_self_training.py:210: UserWarning: y contains no unlabeled samples\n",
            "  warnings.warn(\"y contains no unlabeled samples\", UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SelfTrainingClassifier(base_estimator=RidgeClassifier())"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"Men need war to grow. \n",
        "And friends to go to war with.\"\"\"\n",
        "text = X_CountVectorizer.transform([text])\n",
        "\n",
        "clf.predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f78nfU7PkdkC",
        "outputId": "15372dd4-6b1e-4589-d57a-c202057cd957"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sadness'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# quantam network neural ai"
      ],
      "metadata": {
        "id": "qNpBO6BSkyZn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pip install tensorflow==2.7.0\n",
        "!pip install tensorflow-quantum==0.7.2\n",
        "!pip install pennylane"
      ],
      "metadata": {
        "id": "AHsI2tBlls1B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install cirq==0.13.1"
      ],
      "metadata": {
        "id": "YqNeyxdKltbe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow import keras\n",
        "from keras.models import Model\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "import re\n",
        "\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as p_np\n",
        "\n",
        "from pennylane.templates.state_preparations import MottonenStatePreparation\n",
        "from pennylane.templates.layers import StronglyEntanglingLayers\n",
        "from nltk.corpus import stopwords\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "0aaXa7DTlvm6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/tweet_emotions.csv')\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "metadata": {
        "id": "_hHo8T2alzky"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(y=data[\"sentiment\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "ezdPFhlJmB3c",
        "outputId": "cfcdd318-abc5-4a6c-962f-063332d77bfc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95a6e34250>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download(\"stopwords\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MKP4x0MmisR",
        "outputId": "68d5d623-b75a-4a54-b091-66f59f6ba287"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = set(stopwords.words('english'))\n",
        "from nltk.stem import RegexpStemmer\n",
        "snowball = RegexpStemmer('ing$|s$|e$|able$', min=4)\n",
        "\n",
        "data[\"content\"] = data[\"content\"].apply(lambda x: \" \".join([snowball.stem(re.sub(r'[^\\w\\d]', \"\", word)) for word in x.lower().split() if word not in stop_words]))\n",
        "\n",
        "X = data[\"content\"]\n",
        "tokenize = Tokenizer()\n",
        "\n",
        "tokenize.fit_on_texts(X)\n",
        "\n",
        "max_length = 64\n",
        "vocab_size = len(tokenize.word_index) + 1\n",
        "X = pad_sequences(tokenize.texts_to_sequences(X), maxlen=max_length, padding=\"post\")"
      ],
      "metadata": {
        "id": "C9VfQlXRmC17"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_qubits = 2\n",
        "dev = qml.device(\"default.qubit\", wires=n_qubits)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def qnode(inputs, weights):\n",
        "    qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))\n",
        "    qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))\n",
        "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))\n",
        "\n",
        "weight_shapes = {\"weights\": (3, n_qubits, 3)}\n",
        "\n",
        "qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=2)\n",
        "clayer1 = tf.keras.layers.Dense(2)\n",
        "clayer2 = tf.keras.layers.Dense(13, activation=\"softmax\")\n",
        "model = tf.keras.models.Sequential([clayer1, qlayer, clayer2])\n",
        "opt = tf.keras.optimizers.SGD(learning_rate=0.8)\n",
        "model.compile(opt, loss='mae')"
      ],
      "metadata": {
        "id": "HtmFikd7mEVg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = data['sentiment']\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "func = LabelEncoder()\n",
        "\n",
        "Y = func.fit_transform(Y)\n",
        "Y = tf.convert_to_tensor(Y)\n",
        "\n",
        "X = tf.constant(X)\n",
        "Y = tf.one_hot(Y, depth=1)"
      ],
      "metadata": {
        "id": "Bg_RZP90mF9f"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X,Y, epochs=1, batch_size=256, shuffle=True,steps_per_epoch=1,validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZZcbhtqmM6s",
        "outputId": "770daaf4-aa22-46da-f535-ef5120485fd4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 196s 196s/step - loss: 0.0835 - val_loss: 0.0773\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9545a5c310>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = \"Yes, I am very normal!\"\n",
        "tokenize.fit_on_texts(a)\n",
        "\n",
        "max_length = 64\n",
        "vocab_size = len(tokenize.word_index) + 1\n",
        "a = pad_sequences(tokenize.texts_to_sequences(a), maxlen=max_length, padding=\"post\")"
      ],
      "metadata": {
        "id": "1khZ_ZzfmOeh"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = tf.constant(a)\n",
        "prediction = model.predict(a)"
      ],
      "metadata": {
        "id": "xKsHswf9mRwl"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ap = []\n",
        "for i in range(len(prediction)):\n",
        "    ap.append(np.argmax(prediction[i]))\n",
        "np.argmax(ap)\n",
        "result = np.argmax(ap)\n",
        "result = np.array(result).reshape(1)"
      ],
      "metadata": {
        "id": "lXzxeAApmV7J"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "func.inverse_transform(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVnRcwJFmYx9",
        "outputId": "8e9a8766-057d-460f-c8da-d1a3f194aa4c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['neutral'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "idrEEiZGmZpx"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}