Sentimental Analysis Based Movie Recommender System Using Collaborative Filtering Approach

Abstract

In today's technologically advanced world, a proposal or recommendation engine is possibly the most amazing tool for showcasing. A recommender system is simply a machine learning-based data separation system that predicts a given client's opinions or propensities for a specific product. The problem of data overload in the online business environment is helped by a proposal generator. Because the recommendation system leads the client to the outcomes of the things, he is likely to like, it can therefore help clients save a lot of time browsing. Users are already familiar with the idea of a recommendation engine; whether it be for product suggestions on Amazon, movie suggestions on Netflix, or music suggestions on YouTube, recommender systems are now supporting many aspects of online experience. A Sentimental Analysis is done in order to predict the proposed outcome for a movie system. This analysis involves the comparative analysis of different approaches and their respective accuracies. Cosine Similarity is deployed for better results.