文章基本信息

标题：Movie Rating Prediction using Ensemble Learning Algorithms
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作者：Zahabiya Mhowwala ; A. Razia Sulthana ; Sujala D. Shetty 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：8
DOI：10.14569/IJACSA.2020.0110849
出版社：Science and Information Society (SAI)
摘要：Over the last few decades, social media platforms have gained a lot of popularity. People of all ages, gender, and areas of life have their presence on at least one of the social platforms. The data that is generated on these platforms has been and is being used for better recommendations, marketing activities, forecasting, and predictions. Considering predictions, the movie industry worldwide produces a large number of movies per year. The success of these movies depends on various factors like budget, director, actor, etc. However, it has become a trend to predict the rating of the movie based on the data collected from social media related to the movie. This will help a number of businesses relying on the movie industry in making promotional and marketing decisions. In this report, the aim is to collect movie data from IMDB and its social media data from YouTube and Wikipedia and compare the performance of two machine learning algorithms – Random Forest and XGBoost – best known for their high accuracy with small datasets, but large feature set. The collection of data is done from multiple sources or APIs.
关键词：Machine learning; ensemble learning; random forest algorithm; XGBoost; movie rating prediction