Affiliations: [a] Department of Computer Engineering, Pacific Institute of Technology, Rajasthan, India | [b] Department of Computer Science and Engineering, Pacific University, Udaipur, Rajasthan, India
Correspondence:
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Corresponding author: Vijaya Ravindra Sagvekar, Department of Computer Engineering, Pacific Institute of Technology, Rajasthan, India. E-mails: [email protected] and [email protected].
Abstract: Customer feedback is useful for product development and increases the sales of the product. Reviews on e-commerce websites provided by the user provide valuable information about the product. Sentiment analysis on the text review helps to analyze the sentiment of users about the product and predict the sales of a product. The existing techniques in sentiment analysis use Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models that have limitations of vanishing gradient problem and overfitting problem. Initially, the Amazon review dataset is collected and processed in word embedding stage. The CNN is utilized to extract the features from the input dataset for sentiment analysis. The Word Embedding Attention (WEA) technique provides higher weight to the words having strong relation with class. The CNN feature helps to provide higher performance for a smaller number of training data. This technique helps to increase the performance of the model related class-wise, thus increasing the precision and recall value. Finally, WEA technique in Bi-directional LSTM (BiLSTM) is used to increase the classification performance. The Balanced Cross-Entropy is proposed to maintain the gradient and solves the vanishing gradient problem in the network. The WEA-BiLSTM model has 97.4% accuracy, and 86.8% precision, and the existing CNN model has 97.1% accuracy and 85.4% precision in sentiment analysis. In this study, WEA-LSTM is used for the sentimental analysis of user reviews. This technique solves the vanishing gradient problem in the network by using Balanced Cross Entropy and helps to increase the performance of the model.
Keywords: Balanced cross-entropy, bi-directional long short-term memory, convolutional neural network, sentiment analysis, word embedding attention
