Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity
Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity
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使用社交媒体中基于众包的特征和传统特征来预测电影的受欢迎程度
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Imran Siddiqi
中科院分区：
文献类型：
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作者：
Mehreen Ahmed;Maham Jahangir;H. Afzal;A. Majeed;Imran Siddiqi
预测电影的成功一直是经济学家和投资者（媒体和制作公司）以及预测分析师的兴趣所在。许多属性，如演员阵容，类型，预算，制作公司，PG评级影响电影的受欢迎程度。Twitter、YouTube等社交媒体是人们可以分享他们对电影的看法的主要平台。本文描述了使用机器学习算法对传统特征进行预测分析的实验，这些特征是从网络上的电影数据库以及社交媒体特征（YouTube上的文本评论，推文）中收集的。结果表明，利用社交媒体和其他社交媒体功能的情绪可以比使用传统功能更准确地预测成功。我们分别使用选定的社交媒体功能进行评级和收入预测，获得了77%和61%的最佳值，而选定的传统功能分别获得了76.2%和52%的结果。更多的是，人们发现，这两种类型的属性（传统的和从社交媒体收集的）的混合可以优于在这一领域的现有方法。
Predicting the success of movies has been of interest to economists and investors (media and production houses) as well as predictive analysts. A number of attributes such as cast, genre, budget, production house, PG rating affect the popularity of a movie. Social media such as Twitter, YouTube etc. are major platforms where people can share their views about the movies. This paper describes experiments in predictive analysis using machine learning algorithms on both conventional features, collected from movies databases on Web as well as social media features (text comments on YouTube, Tweets). The results demonstrate that the sentiments harnessed from social media and other social media features can predict the success with more accuracy than that of using conventional features. We achieved best value of 77% and 61% using selected social media features for Rating and Income prediction respectively, whereas selected conventional features gave results of 76.2% and 52% respectively. More it was found that the blend of both types of attributes (conventional and those collected from social media) can outperform the existing approaches in this domain.