Characterization Study of Online Public Opinion Based on Natural Language Processing with Weibo Data

An Zhiyuan
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:188-197, 2024.

Abstract

In the summer of 2022, “Ice cream assassin” has emerged as a prominent buzzword on the Chinese Internet. With its vast user base of 582 million monthly active users, Sina Weibo serves as an ideal platform for analyzing information disseminated within its ecosystem. This platform not only enables us to discern prevailing public sentiment but also facilitates governmental efforts in shaping and regulating the public opinion landscape. This study encompasses a collection of 60,228 Weibo pertaining to “ice cream assassin” posted on Sina Weibo. By employing sentiment analysis algorithm based on Bert’s fine-tuned model, we analyze temporal shifts in emotional trends, summarize changes in public sentiment, and categorize variations in popularity levels. Furthermore, through semantic network analysis, we identify two distinct thematic segments within this realm of public opinion. This study has important implications for assessing the impact of online public opinion on the economy and even society.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-zhiyuan24a, title = {Characterization Study of Online Public Opinion Based on Natural Language Processing with Weibo Data}, author = {Zhiyuan, An}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {188--197}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/zhiyuan24a/zhiyuan24a.pdf}, url = {https://proceedings.mlr.press/v245/zhiyuan24a.html}, abstract = {In the summer of 2022, “Ice cream assassin” has emerged as a prominent buzzword on the Chinese Internet. With its vast user base of 582 million monthly active users, Sina Weibo serves as an ideal platform for analyzing information disseminated within its ecosystem. This platform not only enables us to discern prevailing public sentiment but also facilitates governmental efforts in shaping and regulating the public opinion landscape. This study encompasses a collection of 60,228 Weibo pertaining to “ice cream assassin” posted on Sina Weibo. By employing sentiment analysis algorithm based on Bert’s fine-tuned model, we analyze temporal shifts in emotional trends, summarize changes in public sentiment, and categorize variations in popularity levels. Furthermore, through semantic network analysis, we identify two distinct thematic segments within this realm of public opinion. This study has important implications for assessing the impact of online public opinion on the economy and even society. } }
Endnote
%0 Conference Paper %T Characterization Study of Online Public Opinion Based on Natural Language Processing with Weibo Data %A An Zhiyuan %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-zhiyuan24a %I PMLR %P 188--197 %U https://proceedings.mlr.press/v245/zhiyuan24a.html %V 245 %X In the summer of 2022, “Ice cream assassin” has emerged as a prominent buzzword on the Chinese Internet. With its vast user base of 582 million monthly active users, Sina Weibo serves as an ideal platform for analyzing information disseminated within its ecosystem. This platform not only enables us to discern prevailing public sentiment but also facilitates governmental efforts in shaping and regulating the public opinion landscape. This study encompasses a collection of 60,228 Weibo pertaining to “ice cream assassin” posted on Sina Weibo. By employing sentiment analysis algorithm based on Bert’s fine-tuned model, we analyze temporal shifts in emotional trends, summarize changes in public sentiment, and categorize variations in popularity levels. Furthermore, through semantic network analysis, we identify two distinct thematic segments within this realm of public opinion. This study has important implications for assessing the impact of online public opinion on the economy and even society.
APA
Zhiyuan, A.. (2024). Characterization Study of Online Public Opinion Based on Natural Language Processing with Weibo Data. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:188-197 Available from https://proceedings.mlr.press/v245/zhiyuan24a.html.

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