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Prediction of Movie Success Using Sentimental Analysis and Data Mining
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Author(s): Meenu Vijarania (Center of Excellence, K. R. Mangalam University, Gurugram, India), Ashima Gambhir (Amity University, Gurgaon, India), Deepthi Sehrawat (Amity University, Gurgaon, India)and Swati Gupta (Center of Excellence, K.R. Mangalam University, India)
Copyright: 2022
Pages: 16
Source title:
Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India)and Akshat Agrawal (Amity University, Guragon, India)
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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