Research on sentiment analysis based on the Bilibili video barrage

Research Article
Open access

Research on sentiment analysis based on the Bilibili video barrage

Junyu Bai 1*
  • 1 Northeastern University at Qinhuangdao    
  • *corresponding author 202112165@stu.neuq.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/38/20230551
ACE Vol.38
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-301-2
ISBN (Online): 978-1-83558-302-9

Abstract

Based on the analysis of the emotions and mentality of site B users watching videos, this paper proposes a method to visualize important attributes such as high-frequency words, the proportion of positive and negative comments, and word cloud diagrams. In the context of the rise of the Internet and the increasing application of Web 2.0, this paper took the Bilibili barrage text as the research object. Because of the large amount of barrage data, only individual barrages were selected as the analysis objects according to the requirements. The crawler was used to preprocess the crawled video barrage. Then machine translation knowledge and four algorithms such as the word segmentation algorithm and sentiment analysis algorithm were used to analyze the sentiment of the video barrage from three different dimensions and compare the results. Through the analysis of the visualization results, the differences in the emotional distribution of different video barrages were compared, and two important conclusions were drawn: First, the mentality of Bilibili users watching videos is positive; second, there is a certain correlation between the content of the video and the emotional orientation of the barrage, and mutual prediction can be made between the two. However, the research in this paper is only the tip of the iceberg in the research of public opinion analysis. At present, the application of sentiment analysis in public opinion still faces difficulties. How to optimize the algorithm model according to the current situation requires researchers to conduct deeper research and more extensive thinking.

Keywords:

Sentiment Analysis, Machine Learning, Algorithms, Comparison

Bai,J. (2024). Research on sentiment analysis based on the Bilibili video barrage. Applied and Computational Engineering,38,184-191.
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References

[1]. Yang Wenting. Research and implementation of sentiment analysis algorithm based on microblog [D]. Southwest Jiaotong University, 2015.

[2]. YANG Jian, CHEN Wei. Research on Three Web Crawler Technologies Based on Python [J]. Software Engineering, 2023, Vol. 26(2): 24-27,19.

[3]. Yu Hui, Xie Jun, Xiong Hao, et al. Statistical machine translation method based on chapter context [J]. Journal of Chinese Information Technology, 2013, 27(2): 86-91.

[4]. Zhang Qiyu, Zhu Ling and Zhang Yaping. Review of Chinese word segmentation algorithm [J]. Information Exploration, 2008(11): 4.

[5]. Lin Dongsheng. Research and implementation of Chinese word segmentation algorithm [D]. Northwest University, 2011.

[6]. Cai Jinzhu, Lai Youfu, Han Lulu, et al. Research on image recognition algorithm of wind turbine based on visualization [J]. Modern Industrial Economics and Informatization, 2023, Vol. 13(5): 277-280.

[7]. Wu Yingliang, Wei Gang, Li Haizhou. A Chinese word segmentation algorithm based on the n-gram model and machine learning [J]. Journal of electronics & information technology, 2001, 23(11): 6.

[8]. Yu Chuanming, Yuan Sai, Wang Feng, et al. Research on the scale adaptation of text sentiment analysis algorithm in big data environment: using Twitter as data source [J]. Library and Information Service, 2019, 63(4): 11.

[9]. Wang Xia, Zheng Ning, Xu Ming, et al. Bayesian mail filtering model based on Chinese inflection word matching [J]. Computer Applications and Software, 2010(1): 4.

[10]. Wei Xinyu, Li Binyong, Chen Hongdou et al. Filter and Analysis of Sensitive Words on Web Page Based on Public Opinion Data [J]. Cybersecurity Technology and Application, 2022, (7): 38-39.

[11]. Liu Tengjie. Application of sentiment analysis in tourism research: review and prospect [J]. Tourism Overview, 2023, (1): 48-52.

Cite this article

Bai,J. (2024). Research on sentiment analysis based on the Bilibili video barrage. Applied and Computational Engineering,38,184-191.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-301-2(Print) / 978-1-83558-302-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.38
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Yang Wenting. Research and implementation of sentiment analysis algorithm based on microblog [D]. Southwest Jiaotong University, 2015.

[2]. YANG Jian, CHEN Wei. Research on Three Web Crawler Technologies Based on Python [J]. Software Engineering, 2023, Vol. 26(2): 24-27,19.

[3]. Yu Hui, Xie Jun, Xiong Hao, et al. Statistical machine translation method based on chapter context [J]. Journal of Chinese Information Technology, 2013, 27(2): 86-91.

[4]. Zhang Qiyu, Zhu Ling and Zhang Yaping. Review of Chinese word segmentation algorithm [J]. Information Exploration, 2008(11): 4.

[5]. Lin Dongsheng. Research and implementation of Chinese word segmentation algorithm [D]. Northwest University, 2011.

[6]. Cai Jinzhu, Lai Youfu, Han Lulu, et al. Research on image recognition algorithm of wind turbine based on visualization [J]. Modern Industrial Economics and Informatization, 2023, Vol. 13(5): 277-280.

[7]. Wu Yingliang, Wei Gang, Li Haizhou. A Chinese word segmentation algorithm based on the n-gram model and machine learning [J]. Journal of electronics & information technology, 2001, 23(11): 6.

[8]. Yu Chuanming, Yuan Sai, Wang Feng, et al. Research on the scale adaptation of text sentiment analysis algorithm in big data environment: using Twitter as data source [J]. Library and Information Service, 2019, 63(4): 11.

[9]. Wang Xia, Zheng Ning, Xu Ming, et al. Bayesian mail filtering model based on Chinese inflection word matching [J]. Computer Applications and Software, 2010(1): 4.

[10]. Wei Xinyu, Li Binyong, Chen Hongdou et al. Filter and Analysis of Sensitive Words on Web Page Based on Public Opinion Data [J]. Cybersecurity Technology and Application, 2022, (7): 38-39.

[11]. Liu Tengjie. Application of sentiment analysis in tourism research: review and prospect [J]. Tourism Overview, 2023, (1): 48-52.

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