A Survey of Content-Based Movie Recommendation System with Sentiment Analysis Based on Machine Learning

A Survey of Content-Based Movie Recommendation System with Sentiment Analysis Based on Machine Learning.

Yash Kulkarni, Karan Bhat, Priti Narke, Prajwal Prince, P.M Tekade.
Information Technology department, JSPM’s RSCOE Pune

ABSTRACT

In this digital era, there are endless varieties of content to be consumed like books, videos, articles, movies, etc., it has become a hectic task to find the content of one’s liking. Whereas, the digital content providers want to get as many users on their service as possible for the maximum duration. Here the recommender system comes into play by which the content providers recommend users the stuff according to the users’ liking and preferences.

In this paper, we have proposed a movie recommender system, “Movie Guide”. The goal of Movie Guide is to provide the precise movie recommendations to users. Generally, the basic recommender systems consider any of the below factors for generating recommendations; the choice of the user (i.e. content-based filtering) or the choice of similar users (i.e. collaborative filtering). To create a stable and on-point recommender system a hybrid of content-based filtering as well as collaborative filtering is to be used. Movies alike to the movie of user’s liking are recommended to user by the Content Based recommender system by analyzing the sentiments on the reviews given by the user for that movie.

KEYWORDS: Movies, Recommendation system, CBF- Content-based filtering, CF- Collaborative filtering, Hybrid systems