A Sentiment and Interest Based Approach for Product Recommendation

摘要

The growing popularity of social networks has led to abundant availability of user sentiments, making them a crucial factor in buying decisions, public opinions, and brand reputations. Rise of real-time web has provided an opportunity to utilise time-sensitive data which is available in the form of tweets on public and private Twitter streams, as the basis for product recommendation based on Sentiment analysis. In this paper, we propose a model working on the basics of Interest graph in conjunction with Sentiment analysis to compute the correlation between different entities and provide recommendations, which range from whom to follow on Twitter to what to buy online.