Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)

<Previous Article In Volume
Next Article In Volume>

Sentiment Analysis of Chinese Weibo Trending Topics based on the BERT Model

Authors
Sitao Lu1, *
1School of Software Engineering, Beijing University of Technology, Beijing, 100124, China
*Corresponding author. Email: lusitao@emails.bjut.edu.cn
Corresponding Author
Sitao Lu
Available Online 27 November 2023.
DOI
10.2991/978-94-6463-300-9_54How to use a DOI?
Keywords
BERT model; Natural Language Processing; Sentiment Analysis; Weibo
Abstract

Currently, the entire global population is experiencing the profound impact of the COVID-19 virus. In response, the Chinese government has implemented various measures to enforce social distancing, aiming to minimize the spread of the disease. Consequently, an increasing number of individuals are turning to social media platforms as a means to express their emotions and share their viewpoints. Among these platforms, Weibo has emerged as one of the largest and most vibrant social networks, boasting a substantial user base. In order to effectively capture and reflect the ongoingcxs social trends, Weibo has in”troduced a feature known as “trending topics,” which highlights the most popular subjects among its users. In this research study, a comprehensive collection of over 130,000 trending topics from the entire year of 2022 was gathered and subsequently analyzed using the BERT (Bidirectional Encoder Representations from Transformers) model. The dataset was subjected to several processing steps, including topic categorization and sentiment analysis, to extract meaningful insights and discern patterns within the data. Through this analysis, a deeper understanding of the prevalent social discourse and sentiment surrounding these trending topics can be achieved.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
Series
Advances in Computer Science Research
Publication Date
27 November 2023
ISBN
10.2991/978-94-6463-300-9_54
ISSN
2352-538X
DOI
10.2991/978-94-6463-300-9_54How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sitao Lu
PY  - 2023
DA  - 2023/11/27
TI  - Sentiment Analysis of Chinese Weibo Trending Topics based on the BERT Model
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 527
EP  - 536
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_54
DO  - 10.2991/978-94-6463-300-9_54
ID  - Lu2023
ER  -