Opinion Analysis of Implicit and Explicit Aspects of Product Reviews using Deep Neuro Fuzzy Network and Deep Maxout Network

Abstract

Online product shopping has dramatically become attributable to the e-commerce industry's tremendous boom. Because of the extensive product choices, effortless shopping, and attractive deals offered by all these platforms, customers or even producers have become familiar with them. By expressing their views and opinions about an item, service, theme, etc. online in the form of reviews, comments on blogging, discussion boards, social platforms, etc., users are making more user-generated content through all these e-commerce websites.Understanding implicit or explicit opinions conveyed in e-commerce posts is beneficial for many stake holders. The feature based sentiment analysis helps the customer to take an informed decision. In this paper, devised a new Hybrid Deep Learning Network to do the task effectively. The Aspect Term Extraction stage retrieves the BERT tokens and utilizes these to extract relevant aspects from the provided data. The aspect phrases are indeed taken into consideration and processed through the phase of feature extraction for predicting the sentiment rating. Senti-word Net, Word length, TF-IDF, Elongated Words, and BoW are all a handful of the aspects obtained during the process of feature extraction and then sent to the Hybrid Deep Learning network. The DNFN and DMN are utilized to develop the hybrid deep learning network, and the results from both models are integrated using weight correlation to provide the final sentiment score. The developed Hybrid DL network demonstrated impressive performance with the highest precision, recall, and F1-score compared to existing methods.