MOVIE RECOMMENDATION SYSTEM USING SENTIMENT ANALYSIS FROM MICRO BLOGGING DATA

Abstract

Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media.
Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through
these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the
task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies
that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging
sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the
movie. Experiments conducted on the public database have yielded promising results.