Movie Recommendation and Sentiment Analysis using Deep Learning Algorithms

Abstract

Data available on the internet is growing constantly. This huge amount of data makes it harder for the users to get useful information. Comments and reviews of movies are given by many users, and recommendation systems makes it easier to find useful content, which is fast and relevant for users. Movies with more positive reviews and comments are usually chosen by everyone. The sentiment behind user reviews is useful to know whether a movie is worth watching. This paper outlines an approach for a movie recommendation system that uses cosine similarity technique to recommend similar movies to users based on the movie title , genre, director, actor  they choose or search . Deep learning algorithms, GRU (Gated Recurrent unit), LSTM (long short-term memory), RNN (Recurrent neural network) and BI-LSTM (bi-directional long short-term memory), are trained and tested to classify user comments taken from YouTube into positive, negative, and neutral sentiments. Accuracy, F1-score, precision, recall measures are utilized to assess the model from every perspective. After comparing four algorithms, as BI-LSTM outperformed other algorithms with an accuracy, recall,  F1 score and precision,  of 98.58%, 97.99% 98.26% and 98.54% respectively, Bi-LSTM (bi-directional long short-term memory) is used for performing sentiment analysis on the movie reviews dataset to understand users sentiment.