LDA-Based Topic Mining of Audience Reviews for the “Ne Zha” Animated Film Series

DOI

10.2991/978-94-6239-737-8_63How to use a DOI?

Keywords

Uses and Gratifications Theory; LDA topic modeling; Ne Zha; audience reviews

Abstract

The *Ne Zha* animated film series stands as a land-mark adaptation of domestic mythological IP. To accurately capture audiences’ core demands, this paper adopts the Uses and Gratifications Theory as the theoretical framework, and employs the LDA topic model and SnowNLP sentiment analysis method. Based on comment data mined from four major platforms, including Xiaohongshu, Douyin, Bilibili, and Douban, six major themes, such as content quality, cultural value, and immersive experience, are extracted, and the satisfaction degree and differences of audience demands are analyzed. The results indicate that audiences generally hold positive sentiments, with the highest satisfaction level for industrial identity demand. Meanwhile, the films suffer from low satisfaction of immersive experience demands, uneven distribution of communication resources, and insufficient in depth cultural exploration. Corresponding optimization strategies are proposed in this study, providing empirical references for the high quality development of domestic animated films.

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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TY  - CONF
AU  - Rui Chen
AU  - Ziyi Wei
AU  - Xiaoying Qiao
PY  - 2026
DA  - 2026/08/18
TI  - LDA-Based Topic Mining of Audience Reviews for the “Ne Zha” Animated Film Series
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 523
EP  - 536
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_63
DO  - 10.2991/978-94-6239-737-8_63
ID  - Chen2026
ER  -