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Jim, J. R., Talukder, M. A. R., Malakar, P., Kabir, M. M., Nur, K., & Mridha, M. F. (2024). Recent Advancements and Challenges of NLP-Based Sentiment Analysis: A State-of-the-Art Review. Natural Language Processing Journal, 6, Article 100059.
https://doi.org/10.1016/j.nlp.2024.100059

has been cited by the following article:

• TITLE: Research on Online Public Opinion Patterns under the Boundary between Local and Non-Local Students in DSE: A Case Study of Social Media Texts and Comments

  AUTHORS: Eugene Chan, Chang Liu

  KEYWORDS: Online Public Opinion Analysis, Sentiment, Local and Non-Local Students, Social Media, DSE

  JOURNAL NAME: Advances in Journalism and Communication, Vol.14 No.1, March 23, 2026

  ABSTRACT: This study focuses on the topic of local and non-local students in the Hong Kong Diploma of Secondary Education (DSE), collecting 1203 social media texts and comment data from Douyin and Xiaohongshu (Little Red Book) platforms, employing sentiment analysis and topic identification techniques to explore the characteristics of online public opinion patterns. The research findings reveal that overall public opinion exhibits a neutral tendency (79.8%), with an average sentiment score of 0.14. DSE-related discussions totaled 216 entries, local student-related discussions 146 entries, non-local student-related discussions 41 entries, and comparative discussions 35 entries. Significant emotional differences exist among different groups, with a standard deviation of 0.11, where the comparative discussion group shows the most negative sentiment (−0.23), reflecting the complexity and sensitivity of educational equity issues.