{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analiza sentymentu" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib\n", "import tensorflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Użyty dataset: https://www.kaggle.com/datasets/abhi8923shriv/sentiment-analysis-dataset" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "df_train = pd.read_csv(\"train.csv\", encoding='unicode_escape')\n", "df_test = pd.read_csv(\"test.csv\", encoding='unicode_escape')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(27481, 2)\n" ] }, { "data": { "text/html": [ "
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textsentiment
0I`d have responded, if I were goingneutral
1Sooo SAD I will miss you here in San Diego!!!negative
2my boss is bullying me...negative
3what interview! leave me alonenegative
4Sons of ****, why couldn`t they put them on t...negative
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" ], "text/plain": [ " text sentiment\n", "0 I`d have responded, if I were going neutral\n", "1 Sooo SAD I will miss you here in San Diego!!! negative\n", "2 my boss is bullying me... negative\n", "3 what interview! leave me alone negative\n", "4 Sons of ****, why couldn`t they put them on t... negative" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_df = df_train[['text', 'sentiment']]\n", "\n", "print(review_df.shape)\n", "review_df.head(5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16363, 2)\n" ] }, { "data": { "text/html": [ "
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textsentiment
1Sooo SAD I will miss you here in San Diego!!!negative
2my boss is bullying me...negative
3what interview! leave me alonenegative
4Sons of ****, why couldn`t they put them on t...negative
62am feedings for the baby are fun when he is a...positive
\n", "
" ], "text/plain": [ " text sentiment\n", "1 Sooo SAD I will miss you here in San Diego!!! negative\n", "2 my boss is bullying me... negative\n", "3 what interview! leave me alone negative\n", "4 Sons of ****, why couldn`t they put them on t... negative\n", "6 2am feedings for the baby are fun when he is a... positive" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_df = review_df[review_df['sentiment'] != 'neutral']\n", "\n", "print(review_df.shape)\n", "review_df.head(5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 0, 0, ..., 0, 1, 1], dtype=int64),\n", " Index(['negative', 'positive'], dtype='object'))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sentiment_label = review_df.sentiment.factorize()\n", "sentiment_label" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([' Sooo SAD I will miss you here in San Diego!!!',\n", " 'my boss is bullying me...', ' what interview! leave me alone',\n", " ...,\n", " ' I`ve wondered about rake to. The client has made it clear .NET only, don`t force devs to learn a new lang #agile #ccnet',\n", " ' Yay good for both of you. Enjoy the break - you probably need it after such hectic weekend Take care hun xxxx',\n", " ' But it was worth it ****.'], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet = review_df.text.values\n", "tweet" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.preprocessing.text import Tokenizer\n", "\n", "tokenizer = Tokenizer(num_words=5000)\n", "tokenizer.fit_on_texts(tweet)\n", "\n", "#tutaj potrzebne jest pozbycie się neutralnych opinii" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "encoded_docs = tokenizer.texts_to_sequences(tweet)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "\n", "padded_sequence = pad_sequences(encoded_docs, maxlen=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Klasyfikator z użyciem LSTM" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential_3\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_2 (Embedding)         │ ?                      │   0 (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ spatial_dropout1d_2             │ ?                      │             0 │\n",
       "│ (SpatialDropout1D)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (LSTM)                   │ ?                      │   0 (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (Dropout)             │ ?                      │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM,Dense, Dropout, SpatialDropout1D\n", "from tensorflow.keras.layers import Embedding\n", "\n", "embedding_vector_length = 32\n", "vocab_size = 200\n", "\n", "model = Sequential()\n", "model.add(Embedding(vocab_size, embedding_vector_length, input_length=200))\n", "model.add(SpatialDropout1D(0.25))\n", "model.add(LSTM(50, dropout=0.5, recurrent_dropout=0.5))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(1, activation='sigmoid'))\n", "model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])\n", "\n", "print(model.summary())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m410/410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 96ms/step - accuracy: 0.5876 - loss: 0.6557 - val_accuracy: 0.7666 - val_loss: 0.4750\n", "Epoch 2/5\n", "\u001b[1m410/410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 94ms/step - accuracy: 0.7560 - loss: 0.4941 - val_accuracy: 0.7904 - val_loss: 0.4362\n", "Epoch 3/5\n", "\u001b[1m410/410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 97ms/step - accuracy: 0.7674 - loss: 0.4661 - val_accuracy: 0.7941 - val_loss: 0.4266\n", "Epoch 4/5\n", "\u001b[1m410/410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 96ms/step - accuracy: 0.7792 - loss: 0.4545 - val_accuracy: 0.7932 - val_loss: 0.4305\n", "Epoch 5/5\n", "\u001b[1m410/410\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 98ms/step - accuracy: 0.7876 - loss: 0.4464 - val_accuracy: 0.7962 - val_loss: 0.4187\n" ] } ], "source": [ "history = model.fit(padded_sequence,sentiment_label[0],validation_split=0.2, epochs=5, batch_size=32)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def predict_sentiment(text):\n", " tw = tokenizer.texts_to_sequences([text])\n", " tw = pad_sequences(tw,maxlen=200)\n", " prediction = int(model.predict(tw).round().item())\n", " predicted_label = sentiment_label[1][prediction]\n", " return predicted_label" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "\n", "X_test = df_test[df_test['sentiment'] != 'neutral']\n", "X_test = X_test['text'].astype(str)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m 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"print(len(y_test))" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([1, 0, 1, ..., 0, 1, 1], dtype=int64), Index(['negative', 'positive'], dtype='object'))\n" ] } ], "source": [ "y_labels = y_test.sentiment.factorize(list(sentiment_label[1])) \n", "print(y_labels)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " negative 0.75 0.90 0.82 1001\n", " positive 0.89 0.72 0.80 1103\n", "\n", " accuracy 0.81 2104\n", " macro avg 0.82 0.81 0.81 2104\n", "weighted avg 0.82 0.81 0.81 2104\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TD-IDF - Logistic Regression" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "X_train = df_train[df_train['sentiment'] != 'neutral']\n", "X_train = X_train['text'].astype(str)\n", "\n", "X_test = df_test[df_test['sentiment'] != 'neutral']\n", "X_test = X_test['text'].astype(str)\n", "\n", "vectorizer = TfidfVectorizer()\n", "X_train_tfidf = vectorizer.fit_transform(X_train)\n", "X_test_tfidf = vectorizer.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\48690\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\utils\\validation.py:1107: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "data": { "text/html": [ "
LogisticRegression(max_iter=1000)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LogisticRegression(max_iter=1000)" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train = df_train[df_train['sentiment'] != 'neutral']\n", "y_train = y_train[['sentiment']]\n", "\n", "model = LogisticRegression(solver=\"lbfgs\", penalty=\"l2\", max_iter=1000)\n", "model.fit(X_train_tfidf, y_train)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test_tfidf)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " negative 0.86 0.90 0.88 1001\n", " positive 0.90 0.87 0.88 1103\n", "\n", " accuracy 0.88 2104\n", " macro avg 0.88 0.88 0.88 2104\n", "weighted avg 0.88 0.88 0.88 2104\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TD-IDF - Linear SVM" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "vectorizer = TfidfVectorizer()\n", "X_train_tfidf = vectorizer.fit_transform(X_train)\n", "X_test_tfidf = vectorizer.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\48690\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1118: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "data": { "text/html": [ "
MLPClassifier(max_iter=1000, solver='lbfgs')
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" ], "text/plain": [ "MLPClassifier(max_iter=1000, solver='lbfgs')" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train = df_train[df_train['sentiment'] != 'neutral']\n", "y_train = y_train[['sentiment']]\n", "\n", "model = MLPClassifier(solver=\"lbfgs\", max_iter=1000)\n", "model.fit(X_train_tfidf, y_train)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test_tfidf)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " negative 0.83 0.84 0.83 1001\n", " positive 0.85 0.84 0.85 1103\n", "\n", " accuracy 0.84 2104\n", " macro avg 0.84 0.84 0.84 2104\n", "weighted avg 0.84 0.84 0.84 2104\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TD-IDF - Random Forest" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "vectorizer = TfidfVectorizer()\n", "X_train_tfidf = vectorizer.fit_transform(X_train)\n", "X_test_tfidf = vectorizer.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\48690\\AppData\\Local\\Temp\\ipykernel_12752\\2433069423.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " model.fit(X_train_tfidf, y_train)\n" ] }, { "data": { "text/html": [ "
RandomForestClassifier()
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" ], "text/plain": [ "RandomForestClassifier()" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train = df_train[df_train['sentiment'] != 'neutral']\n", "y_train = y_train[['sentiment']]\n", "\n", "model = RandomForestClassifier()\n", "model.fit(X_train_tfidf, y_train)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test_tfidf)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " negative 0.83 0.89 0.86 1001\n", " positive 0.89 0.84 0.86 1103\n", "\n", " accuracy 0.86 2104\n", " macro avg 0.86 0.86 0.86 2104\n", "weighted avg 0.86 0.86 0.86 2104\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 2 }