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<p>DeepSeek has emerged as a prominent representative of China’s domestic large language models(LLMs), attracting widespread public attention and generating diverse discussions on social media since its release. To systematically examine public sentiment and thematic concerns surrounding DeepSeek, this study analyzes 86,008 Weibo posts collected between January 19 and March 22, 2025. After data cleaning, 59,679 valid entries were retained. A mixed-method approach was adopted, combining Latent Dirichlet Allocation (LDA) for topic modeling and a fine-tuned BERT-based classifier for sentiment analysis. The BERT model, fine-tuned on 10,000 hand-labeled posts, achieved an accuracy of 82% and was subsequently applied to the remaining unlabeled data. Topic modeling revealed nine major themes in public discourse, including chip innovation and industrial transformation, smart device ecosystem, and China–US technological competition. Sentiment analysis showed that 46.3% of the posts expressed a positive attitude, reflecting public endorsement of DeepSeek’s domestic innovation and application potential, although concerns about information security, technical feasibility, and international political dynamics were also evident. Additionally, major events significantly influenced emotional fluctuations, with peaks in public attention observed on January 27 and March 19. These findings offer empirical insights for policymakers, developers, and the broader public into public acceptance, key concerns, and potential risks associated with emerging AI technologies, and support the formulation of informed strategies for technology governance, user engagement, and product optimization.</p>

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Public attitudes toward DeepSeek on Chinese social media: a study based on sentiment analysis and topic modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月6日

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  • Zeqin Lin,
  • Qiuling Huang,
  • Jian Zhong,
  • Jiale Xie,
  • Yu Wang,
  • Weiying He

摘要

DeepSeek has emerged as a prominent representative of China’s domestic large language models(LLMs), attracting widespread public attention and generating diverse discussions on social media since its release. To systematically examine public sentiment and thematic concerns surrounding DeepSeek, this study analyzes 86,008 Weibo posts collected between January 19 and March 22, 2025. After data cleaning, 59,679 valid entries were retained. A mixed-method approach was adopted, combining Latent Dirichlet Allocation (LDA) for topic modeling and a fine-tuned BERT-based classifier for sentiment analysis. The BERT model, fine-tuned on 10,000 hand-labeled posts, achieved an accuracy of 82% and was subsequently applied to the remaining unlabeled data. Topic modeling revealed nine major themes in public discourse, including chip innovation and industrial transformation, smart device ecosystem, and China–US technological competition. Sentiment analysis showed that 46.3% of the posts expressed a positive attitude, reflecting public endorsement of DeepSeek’s domestic innovation and application potential, although concerns about information security, technical feasibility, and international political dynamics were also evident. Additionally, major events significantly influenced emotional fluctuations, with peaks in public attention observed on January 27 and March 19. These findings offer empirical insights for policymakers, developers, and the broader public into public acceptance, key concerns, and potential risks associated with emerging AI technologies, and support the formulation of informed strategies for technology governance, user engagement, and product optimization.