Collaborative Filtering Recommendation System through Sentiment Analysis

Abstract

Purpose: In recent years, the advent of social networking sites has attracted more attention to review-based recommender systems. The purpose of developing such systems is to use the valuable information, which can be obtained from users’ textual reviews. This paper presents a collaborative filtering recommender system using sentiment analysis.

Design / Methodology: For this purpose, a sample of 7210 comments about 221 books from Amazon website are used to sentiment analysis. We used ensemble models to extract users’ opinions. Weighted vote-based classifier ensemble technique is used for ensemble modeling. The required data were collected from Amazon.com through Web Crawlers in Java. The data were limited to Amazon users’ comments to specific book topics such as Business Intelligence. We applied different methods including text normalization and ensemble methods for doing the sentiment analysis.

Finding: The results showed that sentiment analysis of user reviews has a positive effect on recommending popular goods by users and also on the performance of recommender systems.

Practical Implication: These results show that with understanding the effect of sentiment analysis for analysis unstructured data, online retailers could use it for policy making and recommend new suggestion to their customers. Also this system helps consumers to make informed decisions.

Originality/value: This study combines sentiment analysis and recommender systems and shows remarkable improvement in the performance of recommender system.

Keywords: Recommender system, Collaborative filtering, Sentiment analysis, Ensemble models, Weight-based voting .