Sentiment analysis of amazon user reviews using a hybrid approach

Abstract

Consumer reviews of services and products are a critical performance indicator for businesses looking to improve their contributions. They're also important for future customers to understand previous customers' experiences. Sentimental Analysis (SA) identifies patterns of sentiment in reviews by analyzing and extracting opinions from websites, papers, blogs, and other sources. The customer finds it difficult to locate a review for a specific aspect of a product they intend to purchase. Since it is difficult to go through a huge number of reviews, feature selection is used to reduce the data's dimensionality. Because choosing an appropriate feature can identify the product attributes that are disliked or discussed by consumers, selecting a feature is a significant step in sentiment analysis. In this paper, a Pearson correlation coefficient-based Harris Hawks Optimization – based Recurrent Neural Network-Long Short-Term Memory (PCCHHO-RNN-LSTM) algorithm is proposed to select features from reviews given by users for classifying their sentiments according to their appropriate polarity. In this proposed PCCHHO-RNNLSTM, to achieve greater accuracy, the correlation coefficients of features are used to conduct an initial dimensionality reduction. The HHO algorithm is then used to choose a small group of non-redundant features and RNN-LSTM is used to classify sentiments to their suitable polarity. The proposed method has been evaluated using the data gathered from Amazon. com's online product reviews. MATLAB software is used to implement the proposed work. From experimental results, the proposed PCCHH-RNNLSTM demonstrates considerable improvements of 95.8% accuracy, 95.4% precision, 95.6% recall, and 95.2% F-measure, respectively.