Research of TV Dramas Heat Based on Statistical Methods and Deep Learning

Qing Dao No.2 Senior High Jiale Li Yunzhu Gu Bowen Wang

Publications of CMSA of Harvard mathscidoc:1801.38002

Yau Science Award (Math), 2017.8
Very few previous studies have examined the forecast and delimiting of TV dramas evaluating indicators problem from the machine learning and statistic perspective. In this paper, we designed a series of web crawlers for collecting TV-drama-related indicators as raw data. The accurate prediction of the TV drama audience ratings and online views is achieved by the ARIMA model, RNNs, CLDNNs and RVM model. Statistical methods are applied to analyze and compare the TV ratings and the online views. Factor analysis is used to give a definition and calculation method of heat of TV dramas and rankings of the TV dramas based on heat. Finally, mixed CNNs is employed to predict heat of TV dramas using data of different dimensions. In this paper, web-crawler, traditional statistical method and the state-of-the art deep learning techniques are combined to give a basic application for predicting and ranking in TV drama industry.
Heat of TV dramas, Web Crawler, Factor Analysis, Long Short Term Memory, Relevance Vector Machine, Convolutional Neural Network.
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@inproceedings{qing2017research,
  title={Research of TV Dramas Heat Based on Statistical Methods and Deep Learning},
  author={Qing Dao, Jiale Li, Yunzhu Gu, and Bowen Wang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20180118023150277211894},
  booktitle={Yau Science Award (Math)},
  year={2017},
}
Qing Dao, Jiale Li, Yunzhu Gu, and Bowen Wang. Research of TV Dramas Heat Based on Statistical Methods and Deep Learning. 2017. In Yau Science Award (Math). http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20180118023150277211894.
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