计算机辅助设计与图形学学报

BTVis：基于BERTopic的交互式层次主题建模可视分析系统

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卢文昊 , 邹杭纳 , 汤颖 ^*

计算机辅助设计与图形学学报 | 可视化与可视分析 2025,37(12): 2112-2130

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计算机辅助设计与图形学学报 |可视化与可视分析 2025 , 37 (12) : 2112 -2130

BTVis：基于BERTopic的交互式层次主题建模可视分析系统

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卢文昊, 邹杭纳, 汤颖^*

作者信息

浙江工业大学计算机科学与技术学院　杭州　310023

通讯作者:

（ytang@zjut.edu.cn）

作者简介:

卢文昊（1999—），男，硕士研究生，主要研究方向为数据分析可视化

邹杭纳（2002—），女，硕士研究生，主要研究方向为数据分析可视化

汤颖（1977—），女，博士，教授，博士生导师，CCF会员，论文通信作者，主要研究方向为大数据分析与可视化、计算机图形学.

BTVis: An Interactive Hierarchical Topic Modelling Visual Analysis System Based on BERTopic

Wenhao Lu, Hangna Zou, Ying Tang^*

Affiliations

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou　310023

出版时间: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00082

文章导航

摘要

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主题建模是自然语言处理中的重要文本挖掘方法，但其建模过程复杂且会生成部分不符合用户期望的结果.为帮助非专家用户理解模型建模过程，高效掌握和修改模型结果，设计了基于BERTopic的交互式可视分析系统. 系统通过以下关键功能提升BERTopic的可解释性与实用性：（1）挖掘并展示BERTopic层次聚类的中间过程，直观地揭示主题生成机制；（2）分析离群文档，揭示其与主题间的潜在关系；（3）提出多粒度局部模型编辑算法，增强BERTopic主题模型准确性；（4）开发基于Web的交互式层次主题模型系统BTVis，支持用户通过可视分析与交互探索理解并提升模型结果. 在TED、豆瓣影评等真实长文本和短文本数据集中进行了定性分析，面向100位参与者设计用户实验，并通过一致性、多样性和稳定性指标和其他模型进行对比，实验结果验证了所提系统的有效性和实用性.

关键词

文本数据 / 主题建模 / 模型提升 / 可视分析

Abstract

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Topic modelling is an important text mining method in natural language processing, but its modeling process is complex and can generate results that do not always align with user expectations. To address this issue and enable non-expert users to understand the modelling process and modify model results quickly, we propose the interactive visual analysis system BTVis based on BERTopic. The system enhances the interpretability and usability of BERTopic through the following key features: (1) explored and visualized the intermediate process of hierarchical clustering of BERTopic to intuitively reveal the topic generation mechanism; (2) analyzed the outlier documents to uncover potential relationships between the outlier documents and the topics; (3) proposed the multi-granular local model editing algorithms to enhance the accuracy of the BERTopic model; (4) implemented the interactive hierarchical topic modeling system BTVis in a web environment, allowing users to enhance BERTopic results through visual analysis and interactive exploration. We conducted experiments including qualitative analysis, user experiments and quantitative tests with multiple real datasets. The results show the effectiveness and practicality of the system.

Key words

text data / topic modelling / model enhancement / visual analysis

引用本文

卢文昊, 邹杭纳, 汤颖. BTVis：基于BERTopic的交互式层次主题建模可视分析系统. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2112 -2130 . DOI: 10.3724/SP.J.1089.2024-00082

Wenhao Lu, Hangna Zou, Ying Tang. BTVis: An Interactive Hierarchical Topic Modelling Visual Analysis System Based on BERTopic[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2112 -2130 . DOI: 10.3724/SP.J.1089.2024-00082

基金

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浙江省自然科学基金重点项目(LZ23F020010)
国家自然科学基金重大项目(72192820)
国家自然科学基金面上项目(61972355)

参考文献

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文献

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参考文献引证文献

排序方式：

[1]

Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022

[2]

Hofmann T. Unsupervised learning by probabilistic latent semantic analysis[J]. Machine Learning, 2001, 42(1-2): 177-196

[3]

Zhao H, Phung D Q, Huynh V, et al. Topic modelling meets deep neural networks: a survey[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence. Freiburg: ijcai.org, 2021: 4713-4720

[4]

Sun F, Liu J, Wu J, et al. BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2019: 1441-1450

[5]

Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[OL]. [2024-02-09]. https://arxiv.org/abs/2203.05794

[6]

McInnes L, Healy J, Astels S. Hdbscan: hierarchical density based clustering[J]. Journal of Open Source Software, 2017, 2(11): Article No.205

[7]

Lee D, Seung H S. Algorithms for non-negative matrix factorization[C]//Proceedings of the 13th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2000: 535-541

[8]

Yan X H, Guo J F, Lan Y Y, et al. A biterm topic model for short texts[C]//Proceedings of the 22nd International Conference on World Wide Web. New York: ACM Press, 2013: 1445-1456

[9]

Qiang J P, Qian Z Y, Li Y, et al. Short text topic modeling techniques, applications, and performance: a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(3): 1427-1445

[10]

Diederik P K, Welling M. Auto-encoding variational Bayes[OL]. [2024-02-09]. https://arxiv.org/abs/1312.6114

[11]

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144

[12]

Scarselli F, Gori M, Tsoi A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2009, 20(1): 61-80

[13]

Srivastava A, Sutton C. Autoencoding variational inference for topic models[OL]. [2024-02-09]. https://openreview.net/forum?id=BybtVK9lg

[14]

Liu L Y, Huang H Y, Gao Y, et al. Neural variational correlated topic modeling[C]//Proceedings of the World Wide Web Conference. New York: ACM Press, 2019: 1142-1152

[15]

Wang R, Zhou D Y, He Y L. ATM: adversarial-neural topic model[J]. Information Processing & Management, 2019, 56(6): Article No.102098

[16]

Zhu Q L, Feng Z, Li X L. GraphBTM: graph enhanced autoencoded variational inference for biterm topic model[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 4663-4672

[17]

Huang Jiajia, Li Pengwei, Peng Min, et al. Review of deep learning-based topic model[J]. Chinese Journal of Computers, 2020, 43(5): 827-855 (in Chinese) (黄佳佳, 李鹏伟, 彭敏, 等. 基于深度学习的主题模型研究[J]. 计算机学报, 2020, 43(5): 827-855)

[18]

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc, 2017: 6000-6010

[19]

Abdelrazek A, Eid Y, Gawish E, et al. Topic modeling algorithms and applications: a survey[J]. Information Systems, 2023, 112: Article No.102131

[20]

Churchill R, Singh L. The evolution of topic modeling[J]. ACM Computing Surveys, 2022, 54(10s): Article No.215

[21]

Thompson L, Mimno D. Topic modeling with contextualized word representation clusters[OL]. [2024-02-09]. https://arxiv.org/abs/2010.12626

[22]

Angelov D. Top2Vec: distributed representations of topics[OL]. [2024-02-09]. https://arxiv.org/abs/2008.09470

[23]

Le Q, Mikolov T. Distributed representations of sentences and documents[C]//Proceedings of the 31st International Conference on Machine Learning. New York: PMLR, 2014: 1188-1196

[24]

Jiang Z H, Chen H D, Zhou R, et al. HealthPrism: a visual analytics system for exploring children's physical and mental health profiles with multimodal data[J]. IEEE Transactions on Visualization and Computer Graphics, 2024, 30(1): 1205-1215

[25]

Choo J, Lee C, Reddy C K, et al. UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 1992-2001

[26]

van der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579-2605

[27]

Ganesan A, Brantley K, Pan S M, et al. LDAexplore: visualizing topic models generated using latent dirichlet allocation[OL]. [2024-02-09]. https://arxiv.org/abs/1507.06593

[28]

Tang Ying, Su Jianjing, Tong Ning. TMvis: a visual analysis system based on LDA topic modelling[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(10): 1728-1738 (in Chinese) (汤颖, 苏建明, 童宁. TMvis: 基于LDA的主题建模可视分析系统[J]. 计算机辅助设计与图形学学报, 2019, 31(10): 1728-1738)

[29]

Zhu C, Cheng Y, Gan Z, et al. MaxVA: fast adaptation of step sizes by maximizing observed variance of gradients[C]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Research Track. Heidelberg: Springer, 2021: 628-643

[30]

Dou W W, Liu S X. Topic- and time-oriented visual text analysis[J]. IEEE Computer Graphics and Applications, 2016, 36(4): 8-13

[31]

Tang Siliang, Cheng Lu, Shao Jian, et al. A survey on news text visualization via probabilistic topic modeling[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(5): 771-782 (in Chinese) (汤斯亮, 程璐, 邵健, 等. 基于概率主题建模的新闻文本可视化综述[J]. 计算机辅助设计与图形学学报, 2015, 27(5): 771-782)

[32]

Xiang Shouxing, Ouyang Fangxin, Liu Shixia. Concept-based visual analysis of dynamic textual data[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(4): 531-541 (in Chinese) (向首兴, 欧阳方昕, 刘世霞. 概念层次的动态文本可视分析[J]. 计算机辅助设计与图形学学报, 2020, 32(4): 531-541)

[33]

Zhang Yi, Shao Yudong, Zhang Jiawan. Visual analysis of topic transition among different sources of text corpora[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(12): 2265-2272 (in Chinese) (张怡, 邵裕东, 张加万. 多源媒体文本主题演变的可视分析[J]. 计算机辅助设计与图形学学报, 2017, 29(12): 2265-2272)

[34]

Lyu Mengya, Wang Xiaolong, Li Kaixuan, et al. High-dimensional multi-attributeed data visual analysis system for negative comment guidance[J]. Journal of Computer-Aided Design & Computer Graphics, 2024, 36(6): 937-947 (in Chinese) (吕梦雅, 王晓龙, 李凯旋, 等. 负面评论引导的高维多元数据可视分析系统[J]. 计算机辅助设计与图形学学报, 2024, 36(6): 937-947)

[35]

Poux-Médard G, Velcin J, Loudcher S. Information interaction profile of choice adoption[C]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Research Track. Heidelberg: Springer, 2021: 103-118

[36]

Dou W W, Yu L, Wang X Y, et al. HierarchicalTopics: visually exploring large text collections using topic hierarchies[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2002-2011

[37]

Hoque E, Carenini G. Interactive topic hierarchy revision for exploring a collection of online conversations[J]. Information Visualization, 2019, 18(3): 318-338

[38]

Yang Y, Yao Q M, Qu H M. VISTopic: a visual analytics system for making sense of large document collections using hierarchical topic modeling[J]. Visual Informatics, 2017, 1(1): 40-47

[39]

Yang W K, Wang X T, Lu J, et al. Interactive steering of hierarchical clustering[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(10): 3953-3967

[40]

Kowsari K, Brown D E, Heidarysafa M, et al. HDLTex: hierarchical deep learning for text classification[C]//Proceedings of the 16th IEEE International Conference on Machine Learning and Applications. Los Alamitos: IEEE Computer Society Press, 2017: 364-371

[41]

Zhang D C, Ying R, Lauw H W. Hyperbolic graph topic modeling network with continuously updated topic tree[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2023: 3206-3216

[42]

El-Assady M, Sevastjanova R, Sperrle F, et al. Progressive learning of topic modeling parameters: a visual analytics framework[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 382-391

[43]

El-Assady M, Sperrle F, Deussen O, et al. Visual analytics for topic model optimization based on user-steerable speculative execution[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1): 374-384

[44]

El-Assady M, Kehlbeck R, Collins C, et al. Semantic concept spaces: guided topic model refinement using word-embedding projections[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1): 1001-1011

[45]

Kim H, Drake B, Endert A, et al. ArchiText: interactive hierarchical topic modeling[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(9): 3644-3655

[46]

Liu C Y, Li J, Shen T S. Refined semantic pattern interaction exploration for multi-attribute documents: a study on subspace topic modeling and visual analysis[J/OL]. Journal of Computer-Aided Design & Computer Graphics: 1-17[2024-02-09]. https://www.jcad.cn/article/doi/10.3724/SP.J.1089.2024-00108 (in Chinese) (刘陈一, 李杰, 沈天舒. 面向多属性文档的精细化语义模式交互探索方法[J/OL]. 计算机辅助设计与图形学学报: 1-17[2024-02-09]. https://www.jcad.cn/article/doi/10.3724/SP.J.1089.2024-00108)

[47]

Kuang D, Park H. Fast rank-2 nonnegative matrix factorization for hierarchical document clustering[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2013: 739-747

[48]

Wu Yingcai, Cui Weiwei, Song Yangqiu, et al. A survey on topic-based visual text analytics[J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(10): 1266-1272 (in Chinese) (巫英才, 崔为炜, 宋阳秋, 等. 基于主题的文本可视分析研究[J]. 计算机辅助设计与图形学学报, 2012, 24(10): 1266-1272)

[49]

Liu S X, Wang X T, Collins C, et al. Bridging text visualization and mining: a task-driven survey[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(7): 2482-2504

[50]

Ye Li, Wang Lei, Zhou Zhiguang, et al. A survey on the visual analytics of narrative structure abstraction[J]. Journal of Computer-Aided Design & Computer Graphics, 2024, 36(4): 503-515 (in Chinese) (叶力, 王磊, 周志光, 等. 基于叙事结构抽象的可视分析方法综述[J]. 计算机辅助设计与图形学学报, 2024, 36(4): 503-515)

[51]

McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction[OL]. [2024-02-09]. https://arxiv.org/abs/1802.03426

[52]

Dieng A B, Ruiz F J R, Blei D M. Topic modeling in embedding spaces[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 439-453

[53]

Bouma G. Normalized (pointwise) mutual information in collocation extraction[C]//Proceedings of the German Society for Computational Linguistics and Language Technology. Tübingen: Gunter Narr Verlag, 2009: 31-40

[54]

El-Assady M, Kehlbeck R, Metz Y, et al. Semantic color mapping: a pipeline for assigning meaningful colors to text[C]//Proceedings of the 4th Workshop on Visualization Guidelines in Research, Design, and Education. Los Alamitos: IEEE Computer Society Press, 2022: 16-22

[55]

Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[OL]. [2024-02-09]. https://arxiv.org/abs/1301.3781

[56]

Hart S G. NASA-task load index (NASA-TLX); 20 years later[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006, 50(9): 904-908

[57]

Bianchi F, Terragni S, Hovy D, et al. Cross-lingual contextualized topic models with zero-shot learning[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: ACL, 2021: 1676-1683

[58]

Wang J Y, Zhang X L. Deep NMF topic modeling[J]. Neuro-computing, 2023, 515: 157-173

[59]

Zhou X X, Bu J J, Zhou S, et al. Improving topic disentanglement via contrastive learning[J]. Information Processing & Management, 2023, 60(2): Article No.103164

[60]

Wu X B, Dong X S, Nguyen T T, et al. Effective neural topic modeling with embedding clustering regularization[C]//Proceedings of the 40th International Conference on Machine Learning. New York: PMLR, 2023: 37335-37357

2025年第37卷第12期

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doi: 10.3724/SP.J.1089.2024-00082

接收时间：2024-02-09
首发时间：2026-10-10
出版时间：2025-12-20

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收稿日期：2024-02-09
修回日期：2024-12-19

基金

浙江省自然科学基金重点项目(LZ23F020010)

国家自然科学基金重大项目(72192820)

国家自然科学基金面上项目(61972355)

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浙江工业大学计算机科学与技术学院　杭州　310023

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（ytang@zjut.edu.cn）

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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