Xing Wang, Shouhua Zhang, and Ivan Smetannikov. 2020. Fiction Popularity Prediction Based on Emotion Analysis. In 2020 International Conference on Control, Robotics and Intelligent System (CCRIS 2020). Association for Computing Machinery, New York, NY, USA, 169–175. DOI:https://doi.org/10.1145/3437802.3437831
Fiction popularity prediction based on emotion analysis
|Author:||Wang, Xing1; Zhang, Shouhua2; Smetannikov, Ivan3|
1ITMO University Saint Petersburg, Russia
2University of Oulu, Oulu, Finland
3Machine Learning lab ITMO University Saint Petersburg, Russia
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202101262673
Association for Computing Machinery,
|Publish Date:|| 2021-01-26
In addition to bringing us knowledge, books also bring us emotional experiences. How do the emotional fluctuations brought by books affect readers’ evaluation of them? What is the difference in emotional fluctuations between books of different popularity? In this paper, we model and analyse the emotional fluctuations of different fiction books with different popularity and study the feasibility of predicting the popularity of fiction books using emotional fluctuations and recurrent neural networks. A new dataset is also generated to support this research and other related researches. Our proposed method obtained the best accuracy of 73.4% for predicting the popularity of fiction books and 41.4% for predicting genres. Some interesting data insights are also extracted from the dataset.
|Pages:||169 - 175|
CCRIS 2020: 2020 International Conference on Control, Robotics and Intelligent System
International Conference on Control, Robotics and Intelligent System
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This research received valuable help from Shouhua Zhang of University of Oulu, Oulu, Finland. This work is financially supported by the Government of the Russian Federation, grant 08-08.
© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CCRIS 2020: 2020 International Conference on Control, Robotics and Intelligent System, https://doi.org/10.1145/3437802.3437831.