Aspect-based product review summarizer

Abstract

Consumers are now relying on product reviews websites to aid them in deciding which product to buy. These sites contain large number of reviews and reading through them is tedious. In this work, we propose building a product review summarizer which will process all the reviews for a product and present them in an easy to read manner. The generated summaries show a list of product features or aspects and their corresponding rating, allowing users in comparing between different products easily. Our system first makes use of an aspect/sentiment extractor to extract the list of aspects and their sentiment words. Sentiment classification is then performed to obtain the polarity of aspects. Finally, these aspects are combined and assigned a rating to form the final summary. The experimental results on various domains have shown that our system is promising.