Public Opinion Monitoring of Sports Stars Based on Text Sentiment Analysis

Authors

  • Lingfeng Yu

DOI:

https://doi.org/10.62051/ijcsit.v4n2.02

Keywords:

Natural language processing, Opinion monitoring, Sports stars

Abstract

Traditional methods of monitoring public opinion often rely on questionnaires or ballots, which are not only time-consuming and labour-intensive, but also difficult to reflect public sentiment changes and trends in real time. With the development of machine learning and natural language processing technologies, it has become possible to automatically analyse large-scale text data by algorithms and identify the emotional tendencies therein. In this paper, the analysis and statistics of public opinion are achieved through relevant algorithmic models.

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References

[1] Chen, X., Liu, H., Fan, F., et al. (2018). Research on crisis public relations of sports stars in China in the era of new media. Journal of Shenyang Sports Institute, 37(6), 30-36.

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[6] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.

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Published

10-10-2024

Issue

Section

Articles

How to Cite

Yu, L. (2024). Public Opinion Monitoring of Sports Stars Based on Text Sentiment Analysis. International Journal of Computer Science and Information Technology, 4(2), 7-14. https://doi.org/10.62051/ijcsit.v4n2.02