Movie Recommender System Using sentiment Analysis

Abstract

Recommendation systems (RSs) have accumulated huge enthusiasm for applications in web
based business and advanced media. Conventional methodologies in RSs incorporate, for example,
collaborative filtering (CF) and content based (CBF) through these approaches that have certain limitations,
for example, the need of earlier client history and propensities for playing out the assignment of
recommendation. The various techniques, Classifiers and the tools are available for sentiment analysis from
which the researchers can achieve good results. In our work we utilized movie tweets and on the basis of
tweets we calculated the sentimental score , we used the classifier Naïve Bayes with Vardar sentiment the
NLP technique and other tools are listed with their use. In this paper different sentiment analysis technique
applied to improve the recommendation systems and generate good recommendations for movies.