Aspect Based Sentiment Analysis for Product Reviews

Abstract

Customer reviews are very important in e-commerce since they influence purchasers’ decisions to buy. Traditional sentiment analysis gives reviews polarities, but it ignores how people feel about certain aspects of the product. Finding the relevant features, such as battery, camera, and pricing, as well as the sentiment expressed for each aspect, is the goal of aspectbased sentiment analysis, or ABSA. Lexicon-based methods, machine learning approaches, and deep learning tactics are some of the methodologies for ABSA that will be examined in this study. To extract characteristics and correctly classify feelings, we use techniques from Natural Language Processing (NLP), such as Named Entity Recognition (NER), dependency parsing, and trained models like BERT. Benchmark data is used to evaluate the proposed model, showing how well it provides deeper sentiment insights. The findings of this study can be used to improve recommendation systems, examine product reviews, and assist companies in precisely understanding the preferences of their clients. Results from this study can be used to improve recommendation systems, evaluate consumer feedback, and assist companies in better understanding the preferences of their clients.