Movie Recommendation System using Sentiment Analysis from Microblogging Data
This work addresses the problem of reducing dependency on prior user history for movie recommendations, though it appears incremental as it builds on existing methods.
The paper tackled the limitations of traditional recommendation systems by proposing a hybrid approach that combines collaborative filtering, content-based filtering, and sentiment analysis of movie tweets from microblogging data, resulting in promising experimental outcomes.
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.