Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Abstract

Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long short-term memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.