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Since the 20th National Congress of the Communist Party of China introduced the “cultural self-confidence and self-strengthening” strategy, China’s cultural industry has experienced significant growth. The animated film “Ne Zha 2” broke records with a box office revenue exceeding 15 billion yuan, highlighting the global influence of Chinese animation. This study examines the audience of this film, utilizing Python-based web crawlers to gather online review data and employing machine learning alongside Transformer models for sentiment analysis. Key findings reveal that core keywords in viewers’ comments include “rebellion,” “destiny,” and “breakthrough,” with emotional highlights focused on “being moved,” “awe,” and “disappointment.“ Among NLP (Natural Language Processing) models, the Transformer model demonstrated superior performance, achieving an accuracy rate of 87.3% in identifying positive review characteristics. This research offers valuable insights for enhancing the viewing experience within the animation film industry.

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Sentiment Analysis of Chinese Animated Film Consumers Based on Text Mining: A Case Study of “Ne Zha 2”

  • 会议论文

  • 发表日期：

    2026年

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  • Lixia Wu,
  • Hanjie Li,
  • Yuxin Wu,
  • Qianqian Wu

摘要

Since the 20th National Congress of the Communist Party of China introduced the “cultural self-confidence and self-strengthening” strategy, China’s cultural industry has experienced significant growth. The animated film “Ne Zha 2” broke records with a box office revenue exceeding 15 billion yuan, highlighting the global influence of Chinese animation. This study examines the audience of this film, utilizing Python-based web crawlers to gather online review data and employing machine learning alongside Transformer models for sentiment analysis. Key findings reveal that core keywords in viewers’ comments include “rebellion,” “destiny,” and “breakthrough,” with emotional highlights focused on “being moved,” “awe,” and “disappointment.“ Among NLP (Natural Language Processing) models, the Transformer model demonstrated superior performance, achieving an accuracy rate of 87.3% in identifying positive review characteristics. This research offers valuable insights for enhancing the viewing experience within the animation film industry.