LSTM and Ensemble Based Approach for Predicting the Success of Movies Using Metadata and Social Media

Abstract

Twitter, for example, offers a wealth of information on people's choices. Because of social media's growing acceptability and popularity, extracting information from data produced on social media has emerged as a prominent study issue. These massive amounts of data are used to build models that anticipate behavior and trends. On Twitter, people express their opinions regarding movies. In this study, a Long Short-Term Memory (LSTM) and ensemble based approach was proposed predicting the success of movies using metadata and social media. In this research, both social media data and movie metadata were consumed to predict the success of the movies. The metadata of the movie also plays an important role, which can be utilized to predict the success of the movies. IMDb ratings, the genre of the movies, and details about the awards that the movies won or nominated are some of the metadata used in addition to the tweets. LSTM, a neural network (NN) model, was applied to identify the sentiment value of the Twitter posts. Then, the ensemble approach was employed to predict the success of movies using movie metadata and results from the LSTM based NN model. This combined model was able to obtain 81.2% accuracy and outperformed the other implemented models.