L2b: Lexicon Boosted Bayesian Classification For Popularity Prediction Of Movies With Improved Accuracy Using Twitter Corpus

Abstract

Social networking plays a vital role in the research phenomenon. It helps in acquiring the most recent news and information consistently with settling on ground-breaking choices and expectations. There are enormous expressions of people opinion in addition to what's circumventing the world. Twitter, is a well known blogging site where individuals place their perspectives and inclinations based on their interest. This paper presents a Lexicon Boosted Bayesian (L2B) classification for popularity prediction of movies based on twitter corpus. The proposed approach predicts the accomplishment of the film utilizing perspective individuals that might actually be accomplished by assessment examination with Naïve Bayes classification and Lexicon approach. In addition, it focuses with the representation of information in R language which stands the best in speaking to the investigation in pictorial arrangement and a guide perception that depicts the area of the tweets where it has come from.  The result analysis proves the effectiveness of proposed technique in comparison with the existing approaches for predicting the success of the movies.