Multi-source Word-aligned Attention Average Pooling based Bidirectional Encoder Representation from Transformers for Product Review Sentiment Analysis

The product review gives critical data for both businesses and consumers, offering insights needed before buying a service or product. However, the existing methods has drawback of there is not understanding semantic relationship among adjacent characters. To overcome these limitations in this research proposed a Multi-source Word-aligned Attention Average Pooling based Bidirectional Encoder Representation from Transformers (MWAAP-BERT) model for product review sentiment analysis. The dataset utilized for the research is Amazon product review dataset and the data is per-processed by several methods. The Term Frequency-Inverse Document Frequency (TF-IDF) and Skip N-gram methods are used for feature extraction and proposed MWAAP-BERT model is used for product review sentiment classification. Performance of proposed method is estimated with performance measure of accuracy, precision, recall and f1-score. Proposed technique attained high accuracy of 97.5 %, precision of 96.5%, recall of 96.5% and f1-score of 96.5% which is superior than other existing methods like Recurrent Neural Network (RNN), Taylor-Harris Hawks Optimization driven Long Short-term Memory (THHO-BiLSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)