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Sentiment Analysis of Amazon Products Review Data using LSTM

Hey Folks, we are back again with another article on the sentiment analysis of amazon electronics review data.

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So, we have processed amazon review data.

Let’s have a look at it.

If you want to see the pre-processing steps that we have done in the previous article you can check out https://medium.com/@sameerbairwa07/sentiment-analysis-of-amazon-product-reviews-93437ad76b59

So we have the review, rating, sentiment for further process.

Now we will do some more pre-processing for tokenization.
1. remove spacial characters
2. remove bad Symbole
3. remove stop words

Stop Words: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.

{‘ourselves’, ‘hers’, ‘between’, ‘yourself’, ‘but’, ‘again’, ‘there’, ‘about’, ‘once’, ‘during’, ‘out’, ‘very’, ‘having’, ‘with’, ‘they’, ‘own’, ‘an’, ‘be’, ‘some’, ‘for’, ‘do’, ‘its’, ‘yours’, ‘such’, ‘into’, ‘of’, ‘most’, ‘itself’, ‘other’, ‘off’, ‘is’, ‘s’, ‘am’, ‘or’, ‘who’, ‘as’, ‘from’, ‘him’, ‘each’, ‘the’, ‘themselves’, ‘until’, ‘below’, ‘are’, ‘we’, ‘these’, ‘your’, ‘his’, ‘through’, ‘don’, ‘nor’, ‘me’, ‘were’, ‘her’, ‘more’, ‘himself’, ‘this’, ‘down’, ‘should’, ‘our’, ‘their’, ‘while’, ‘above’, ‘both’, ‘up’, ‘to’, ‘ours’, ‘had’, ‘she’, ‘all’, ‘no’, ‘when’, ‘at’, ‘any’, ‘before’, ‘them’, ‘same’, ‘and’, ‘been’, ‘have’, ‘in’, ‘will’, ‘on’, ‘does’, ‘yourselves’, ‘then’, ‘that’, ‘because’, ‘what’, ‘over’, ‘why’, ‘so’, ‘can’, ‘did’, ‘not’, ‘now’, ‘under’, ‘he’, ‘you’, ‘herself’, ‘has’, ‘just’, ‘where’, ‘too’, ‘only’, ‘myself’, ‘which’, ‘those’, ‘i’, ‘after’, ‘few’, ‘whom’, ‘t’, ‘being’, ‘if’, ‘theirs’, ‘my’, ‘against’, ‘a’, ‘by’, ‘doing’, ‘it’, ‘how’, ‘further’, ‘was’, ‘here’, ‘than’}

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

Now we will Keras tokenizer to make tokens of words.

MAX_NB_WORDS = 500000              #vary absed on size of dataset
# Max number of words in each complaint.
MAX_SEQUENCE_LENGTH = 250
# This is fixed.
EMBEDDING_DIM = 15
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~', lower=True)
tokenizer.fit_on_texts(df['reviewFinal'].values)
word_index = tokenizer.word_index

The maximum number of words depends on size of dataset.

Here we split the dataset in 80, 20 ratio

Training on 80000
Testing on 20000

Now let’s define a simple LSTM for training.

from keras.models import Sequential
from keras.layers import Input, Dense, Embedding, SpatialDropout1D, add, concatenate
from keras.layers.recurrent import LSTM
from keras.callbacks import ModelCheckpoint, EarlyStoppingmodel = Sequential()
model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=X.shape[1]))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(15, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])epochs = 15
batch_size = 32history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.1,callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])

The batch size is 32 and epochs are 15 for training.

now you might be thinking about why it stopped at 5 because the model val_accuracy stopped increasing after 5 epochs.

Evaluation of Model:

accr = model.evaluate(X_test,Y_test)
print('Test set\n  Loss: {:0.3f}\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))

Plotting accuracy curve and confusion matrix

import matplotlib.pyplot as plt
%matplotlib inline
#plotting curves for LSTMprint(history.history.keys())
#  "Accuracy"
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
# "Loss"
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()

Accuracy Matix

def modelEvaluation(predictions, y_test_set):
    #Print model evaluation to predicted result 

    print ("\nAccuracy on validation set: {:.4f}".format(accuracy_score(y_test_set, predictions)))
    #print "\nAUC score : {:.4f}".format(roc_auc_score(y_test_set, predictions))
    print ("\nClassification report : \n", metrics.classification_report(y_test_set, predictions))
    print ("\nConfusion Matrix : \n", metrics.confusion_matrix(y_test_set, predictions))#making predictions using LSTM
y_hat = model.predict(X_test)
y_hat_class = model.predict_classes(X_test)
y_pred_list = y_hat_class.tolist()
y_test = []
for i in Y_test:
    y_test.append(np.argmax(i))
modelEvaluation(y_pred_list,y_test)

Project Github link: https://github.com/sameerbairwa/Text-Analysis

That’s all about sentiment analysis using machine learning.
In the next article, we apply more deep-learning techniques on the dataset.

AI/ML

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