Proceeding of 2025 8th International Conference on Humanities Education and Social Sciences (ICHESS 2025)

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Cross‑Platform Data‑Driven Analysis of Hot Search Topics and Sentiment Using Multi‑Model Voting

Authors
Weichen Wang1, *
1School of Asian Studies, Beijing Foreign Studies University, Beijing, 100089, China
*Corresponding author. Email: 442797506@qq.com
Corresponding Author
Weichen Wang
Available Online 26 March 2026.
DOI
10.2991/978-2-38476-551-5_89How to use a DOI?
Keywords
Social Networking; Hot Search; Public Opinion Dissemination; Large Language Models; Thematic Analysis
Abstract

This paper investigates hot search texts from Chinese internet platforms to examine the application and methodology of data-driven text research in the new-media context. The research collects over 150,000 hot search entries from three mainstream social platforms, including Weibo, Zhihu, and Toutiao, and constructs a multidimensional analysis framework through data cleaning, classification, processing, and modeling. Specifically, a hot search classification system is developed using a multi-model voting procedure that involves Gemini, Doubao, Kimi, and ChatGPT, resulting in ten final category labels. The classification accuracy of each model is evaluated against manual annotations, from which Gemini emerges as the best-performing classifier. Following classification by Gemini, the study conducts a systematic analysis that combines high-frequency token statistics, SnowNLP-based sentiment analysis, and Ordinary Least Squares regression. The paper provides a methodological reference for data-driven analysis of popular social media texts and emphasizes the integration of multi-model voting and statistical testing to strengthen the reliability and policy relevance of research on hot search topics and sentiment.

Copyright
© 2026 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.

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Volume Title
Proceeding of 2025 8th International Conference on Humanities Education and Social Sciences (ICHESS 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
26 March 2026
ISBN
978-2-38476-551-5
ISSN
2352-5398
DOI
10.2991/978-2-38476-551-5_89How to use a DOI?
Copyright
© 2026 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  - Weichen Wang
PY  - 2026
DA  - 2026/03/26
TI  - Cross‑Platform Data‑Driven Analysis of Hot Search Topics and Sentiment Using Multi‑Model Voting
BT  - Proceeding of 2025 8th International Conference on Humanities Education and Social Sciences (ICHESS 2025)
PB  - Atlantis Press
SP  - 822
EP  - 833
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-551-5_89
DO  - 10.2991/978-2-38476-551-5_89
ID  - Wang2026
ER  -