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Big Data Analysis of TV Dramas Based on Machine Learning

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Smart Computing and Communication (SmartCom 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10699))

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Abstract

Currently, large amount of TV dramas has overwhelmed the demand of TV station which had caused massive waste of resources. This article offers several practical solutions to tackle the above-mentioned problems through building model based on machine learning. Firstly, we build a TV score prediction model with regression and machine learning to rank the most welcomed TV drama. Moreover, we write an Internet worm to collect data from the internet, and build a Star popularity index prediction model by machine learning and regression. And list the much-acclaimed stars based on the popularity index. In conclusion, with the predict score of the TV drama predicted based on machine learning, it can provide a reference for TV station to manage TV programs and with the starring ranking it can help TV drama production team to produce TV dramas in a high quality.

This study was supported by Guangdong Natural Science Foundation (2016A030313036), Shenzhen Science and Technology Foundation (JCYJ20150324140036842) and Guangdong Graduate Education Project (2015SQXX0).

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Authors and Affiliations

  1. Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, Guangdong, People’s Republic of China

    Jiaqi Tan, Feiqiao Mao, Lianghai Yang & Jiahui Wang

Authors
  1. Jiaqi Tan
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  2. Feiqiao Mao
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  3. Lianghai Yang
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  4. Jiahui Wang
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Corresponding author

Correspondence to Feiqiao Mao .

Editor information

Editors and Affiliations

  1. Columbia University, New York, New York, USA

    Meikang Qiu

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Tan, J., Mao, F., Yang, L., Wang, J. (2018). Big Data Analysis of TV Dramas Based on Machine Learning. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2017. Lecture Notes in Computer Science(), vol 10699. Springer, Cham. https://doi.org/10.1007/978-3-319-73830-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-73830-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73829-1

  • Online ISBN: 978-3-319-73830-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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