Movie Recommendation System Using Hybrid Filtering and Sentimental Analysis

As humans’ opinions help enhance products efficiency, and since the success or the failure of a movie depends on its reviews, there is an increase in the demand and need to build a good sentiment analysis model that classifies movies ratings. In this research, tokenization is employed to transfer the input string into a word vector, stemming is utilized to extract the root of the words, feature selection is conducted to extract the essential words, and finally classification is performed to label reviews as being either positive or negative. We build this model simply by using KNN.