  Recommendation systems are important intelligent systems that play a vital
role in providing selective information to users. Traditional approaches in
recommendation systems include collaborative filtering and content-based
filtering. However, these approaches have certain limitations like the
necessity of prior user history and habits for performing the task of
recommendation. In order to reduce the effect of such dependencies, this paper
proposes a hybrid recommendation system which combines the collaborative
filtering, content-based filtering with sentiment analysis of movie tweets. The
movie tweets have been collected from microblogging websites to understand the
current trends and user response of the movie. Experiments conducted on public
database produce promising results.
