378 KiB
378 KiB
Analiza sentymentu
import pandas as pd
import matplotlib
import tensorflowdf_train = pd.read_csv("train.csv", encoding='unicode_escape')
df_test = pd.read_csv("test.csv", encoding='unicode_escape')review_df = df_train[['text', 'sentiment']]
print(review_df.shape)
review_df.head(5)(27481, 2)
| text | sentiment | |
|---|---|---|
| 0 | I`d have responded, if I were going | neutral |
| 1 | Sooo SAD I will miss you here in San Diego!!! | negative |
| 2 | my boss is bullying me... | negative |
| 3 | what interview! leave me alone | negative |
| 4 | Sons of ****, why couldn`t they put them on t... | negative |
review_df = review_df[review_df['sentiment'] != 'neutral']
print(review_df.shape)
review_df.head(5)(16363, 2)
| text | sentiment | |
|---|---|---|
| 1 | Sooo SAD I will miss you here in San Diego!!! | negative |
| 2 | my boss is bullying me... | negative |
| 3 | what interview! leave me alone | negative |
| 4 | Sons of ****, why couldn`t they put them on t... | negative |
| 6 | 2am feedings for the baby are fun when he is a... | positive |
sentiment_label = review_df.sentiment.factorize()
sentiment_label(array([0, 0, 0, ..., 0, 1, 1], dtype=int64), Index(['negative', 'positive'], dtype='object'))
tweet = review_df.text.values
tweetarray([' Sooo SAD I will miss you here in San Diego!!!',
'my boss is bullying me...', ' what interview! leave me alone',
...,
' 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',
' Yay good for both of you. Enjoy the break - you probably need it after such hectic weekend Take care hun xxxx',
' But it was worth it ****.'], dtype=object)from tensorflow.keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(tweet)
#tutaj potrzebne jest pozbycie się neutralnych opiniiencoded_docs = tokenizer.texts_to_sequences(tweet)from tensorflow.keras.preprocessing.sequence import pad_sequences
padded_sequence = pad_sequences(encoded_docs, maxlen=200)Klasyfikator z użyciem LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM,Dense, Dropout, SpatialDropout1D
from tensorflow.keras.layers import Embedding
embedding_vector_length = 32
vocab_size = 200
model = Sequential()
model.add(Embedding(vocab_size, embedding_vector_length, input_length=200))
model.add(SpatialDropout1D(0.25))
model.add(LSTM(50, dropout=0.5, recurrent_dropout=0.5))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
print(model.summary())Model: "sequential_3"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ embedding_2 (Embedding) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ spatial_dropout1d_2 │ ? │ 0 │ │ (SpatialDropout1D) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lstm_2 (LSTM) │ ? │ 0 (unbuilt) │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout_2 (Dropout) │ ? │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ ? │ 0 (unbuilt) │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 0 (0.00 B)
Trainable params: 0 (0.00 B)
Non-trainable params: 0 (0.00 B)
None
history = model.fit(padded_sequence,sentiment_label[0],validation_split=0.2, epochs=5, batch_size=32)Epoch 1/5 [1m410/410[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m43s[0m 96ms/step - accuracy: 0.5876 - loss: 0.6557 - val_accuracy: 0.7666 - val_loss: 0.4750 Epoch 2/5 [1m410/410[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m41s[0m 94ms/step - accuracy: 0.7560 - loss: 0.4941 - val_accuracy: 0.7904 - val_loss: 0.4362 Epoch 3/5 [1m410/410[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 97ms/step - accuracy: 0.7674 - loss: 0.4661 - val_accuracy: 0.7941 - val_loss: 0.4266 Epoch 4/5 [1m410/410[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 96ms/step - accuracy: 0.7792 - loss: 0.4545 - val_accuracy: 0.7932 - val_loss: 0.4305 Epoch 5/5 [1m410/410[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 98ms/step - accuracy: 0.7876 - loss: 0.4464 - val_accuracy: 0.7962 - val_loss: 0.4187
def predict_sentiment(text):
tw = tokenizer.texts_to_sequences([text])
tw = pad_sequences(tw,maxlen=200)
prediction = int(model.predict(tw).round().item())
predicted_label = sentiment_label[1][prediction]
return predicted_labelfrom sklearn.metrics import classification_report
X_test = df_test[df_test['sentiment'] != 'neutral']
X_test = X_test['text'].astype(str)y_pred = []
for txt in X_test:
y_pred.append(predict_sentiment(txt))[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 48ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 46ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 47ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 56ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 40ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 44ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 48ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 42ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 44ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 51ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 49ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 44ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 48ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 42ms/step [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 39ms/step 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y_test = df_test[df_test['sentiment'] != 'neutral']
y_test = y_test[['sentiment']]
print(y_test.head())
print(len(y_test))sentiment 1 positive 2 negative 3 positive 4 positive 5 positive 2104
y_labels = y_test.sentiment.factorize(list(sentiment_label[1]))
print(y_labels)(array([1, 0, 1, ..., 0, 1, 1], dtype=int64), Index(['negative', 'positive'], dtype='object'))
print(classification_report(y_test, y_pred)) precision recall f1-score support
negative 0.75 0.90 0.82 1001
positive 0.89 0.72 0.80 1103
accuracy 0.81 2104
macro avg 0.82 0.81 0.81 2104
weighted avg 0.82 0.81 0.81 2104
TD-IDF - Logistic Regression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegressionX_train = df_train[df_train['sentiment'] != 'neutral']
X_train = X_train['text'].astype(str)
X_test = df_test[df_test['sentiment'] != 'neutral']
X_test = X_test['text'].astype(str)
vectorizer = TfidfVectorizer()
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)y_train = df_train[df_train['sentiment'] != 'neutral']
y_train = y_train[['sentiment']]
model = LogisticRegression(solver="lbfgs", penalty="l2", max_iter=1000)
model.fit(X_train_tfidf, y_train)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(). y = column_or_1d(y, warn=True)
LogisticRegression(max_iter=1000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression(max_iter=1000)
y_pred = model.predict(X_test_tfidf)print(classification_report(y_test, y_pred)) precision recall f1-score support
negative 0.86 0.90 0.88 1001
positive 0.90 0.87 0.88 1103
accuracy 0.88 2104
macro avg 0.88 0.88 0.88 2104
weighted avg 0.88 0.88 0.88 2104
TD-IDF - Linear SVM
from sklearn.neural_network import MLPClassifiervectorizer = TfidfVectorizer()
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)y_train = df_train[df_train['sentiment'] != 'neutral']
y_train = y_train[['sentiment']]
model = MLPClassifier(solver="lbfgs", max_iter=1000)
model.fit(X_train_tfidf, y_train)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(). y = column_or_1d(y, warn=True)
MLPClassifier(max_iter=1000, solver='lbfgs')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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MLPClassifier(max_iter=1000, solver='lbfgs')
y_pred = model.predict(X_test_tfidf)print(classification_report(y_test, y_pred)) precision recall f1-score support
negative 0.83 0.84 0.83 1001
positive 0.85 0.84 0.85 1103
accuracy 0.84 2104
macro avg 0.84 0.84 0.84 2104
weighted avg 0.84 0.84 0.84 2104
TD-IDF - Random Forest
from sklearn.ensemble import RandomForestClassifiervectorizer = TfidfVectorizer()
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)y_train = df_train[df_train['sentiment'] != 'neutral']
y_train = y_train[['sentiment']]
model = RandomForestClassifier()
model.fit(X_train_tfidf, y_train)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(). model.fit(X_train_tfidf, y_train)
RandomForestClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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RandomForestClassifier()
y_pred = model.predict(X_test_tfidf)print(classification_report(y_test, y_pred)) precision recall f1-score support
negative 0.83 0.89 0.86 1001
positive 0.89 0.84 0.86 1103
accuracy 0.86 2104
macro avg 0.86 0.86 0.86 2104
weighted avg 0.86 0.86 0.86 2104