Integrated topic modeling and sentiment analysis: A review rating prediction approach for recommender systems

Abstract

Recommender systems (RSs) are running behind E-commerce websites to recommend items that are likely to be bought by users. Most of the existing RSs are relying on mere star ratings while making recommendations. However, ratings alone cannot help RSs make accurate recommendations, as they cannot properly capture sentiments expressed towards various aspects of the items. The other rich and expressive source of information available that can help make accurate recommendations is user reviews. Because of their voluminous nature, reviews lead to the information overloading problem. Hence, drawing out the user opinion from reviews is a decisive job. Therefore, this paper aims to build a review rating prediction model that simultaneously captures the topics and sentiments present in the reviews which are then used as features for the rating prediction. A new sentiment-enriched and topic-modeling-based review rating prediction technique which can recognize modern review contents is proposed to facilitate this feature. Experimental results show that the proposed model best infers the rating from reviews by harnessing the vital information present in them.