Multilingual Opinion Mining Movie Recommendation System Using RNN

Abstract

Twitter is a news and social networking site where people around the world post their blogs and share their feeling, point of view, and comments regarding any communication or about any latest movie, etc. Thus, Twitter generates a massive quantity of Twitter data every day. This data is real time, which is being used in the proposed work for implementing a “movie recommendation system.” To enhance the performance of the framework, sentimental analysis is also being applied to the data. Nowadays, the recommendation system is also an essential tool for online businesses and used by various e-commerce sites, music applications, entertainment sites, etc. This work proposed a movie recommendation system for the movie domain which is developed using real-time multilingual tweets. These tweets are obtained from Twitter API using the LinqToTwitter Library. Sentimental analysis is also being performed on tweets. In this work, multilingual and real-time tweets are considered. These tweets are translated into the target language using Google Translate API. The proposed work used the Stanford library for preprocessing, and RNN is used for classifying the tweets. The tweets are classified as positive, negative, and neutral tweets. Preprocessing of the tweets is done to remove unwanted words, URLs, emoticons, etc. Finally, based on the classification, the movie is suggested to the user. This proposed work is better than the current practices as the implementation is being done on real-time tweets, and sentimental analysis is also being performed to get better results. This system is achieving 91.67% accuracy, 92% precision, 90.2% recall, and 90.98% f-measure.