Prediction of Movie Success Using Sentimental Analysis and Data Mining

Prediction of Movie Success Using Sentimental Analysis and Data Mining

Meenu Vijarania, Ashima Gambhir, Deepthi Sehrawat, Swati Gupta
Copyright: © 2022 |Pages: 16
DOI: 10.4018/978-1-7998-9012-6.ch008
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Abstract

Movies have become a significant part of today's generation. In this chapter, the authors worked on data mining and ML techniques like random forest regression, decision tree regression, support vector regression, and predict the success of the movies on the basis of ratings from IMDb and data retrieved from comments on social media platforms. Based on ML techniques, the chapter develops a model that will predict movie success before the release of the movie and thereby decrease the risk. Twitter sentimental analysis is used to retrieve data from Twitter, and polarity and subjectivity of the movie is calculated based on the user reviews, and those retrieved data machine learning algorithms are used to predict the IMDb rating. A predictive model is developed by using three algorithms, decision tree regression, SVR, and random forest regression. The chapter compared the results using three different techniques to get the movie success prediction at a reasonable accuracy.
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Literature Review

The fact of how the movie has been justified primarily decides the success of a movie. Initially, many people considered gross collection as the major point for movie success. But now many factors affect the same like a review of the movie, background story, box office collection, likes and comments about the movies. Many used regression models and considered revenues and applied binary classification for the forecast. At the same time, few adopted applications of Natural Language Processing for sentimental analysis and gathered movie reviews for their testing. There are many works done, predicting the success or failure of the movie. Some were predicting result after the movie is released, while some were predicting before release. We have studied multiple papers which we are discussing below:

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