Analyzing the 'Belt and Road' topic of overseas Chinese media based on the BERTopic modeling
Authors: 
Huiying Yan, Yu ZhangAuthors Info & Claims
Pages 62 - 69
Abstract
The year 2023 marks the 10th anniversary of the "Belt and Road" initiative. Chinese-language media abroad serves as an important channel for overseas Chinese to access information, and is a window for external communication and cultural exchange, as well as an important force in the construction of the "Belt and Road". In order to better explore the theme information related to the "Belt and Road" in overseas Chinese-language media, and integrate the idea of experimental engineering, an orthogonal experimental method was used to study 4 important parameters of the BERTopic model. The best parameters were selected by analyzing the main effects and constructing the BERTopic model, and the content and evolution trends of the topics were analyzed based on the model results. This method provides a new approach for text topic data mining and important reference for BERTopic topic modeling.
References
[1]
A. Paccanaro and G. E. Hinton. 2001. Learning distributed representations of concepts using linear relational embedding. in IEEE Transactions on Knowledge and Data Engineering. ACM 2, 4 (April 2001),232-244. https://doi.org/10.1109/69.917563.
[2]
BENGIO Y, COURVILLE A, VINCENT P. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence. ACM 35, 8 (August 2013), 1798-828.https://doi.org/10.1109/TPAMI.2013.50
[3]
Tomas M, Kai C, Greg C, Jeffrey D. 2013. Efficient estimation of word representations in vector space., Computation and Language. ACM 7, 9 (September 2013), 289-296. https://doi.org/10.48550/arXiv.1301.3781
[4]
DEERWESTER S, DUMAIS S T, FURNAS G W, 1990. Indexing by latent semantic analysis. Journal of the American society for information science. ACM 41,6.(June 1990), 391-407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6%3c391::AID-ASI1%3e3.0.CO;2-9
[5]
PENNINGTON J, SOCHER R, MANNING C D. Glove.2014. Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing(EMNLP). Doha, Qata, CA.1532-1543. https://doi.org/10.3115/v1/D14-1162
[6]
KENTON J D M-W C, Toutanova L K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the NAACL,.Minneapolis, Minnesota, June, CA, 4171-4186. https://doi.org/10.48550/arXiv.1810.04805
[7]
PETERS M E, NEUMANN M, IYYER M, 2018. Deep Contextualized Word Representations. Proceedings of the 2018 Conference of the NAACL, New Orleans, Louisiana, CA, 2227-2237. https://doi.org/10.48550/arXiv.1802.05365
[8]
VASWANI A, SHAZEER N, PARMAR N, 2017. Attention is all you need. Advances in neural information processing systems. ACM 12, 6 (June 2017), https://doi.org/10.48550/arXiv.1706.03762
[9]
KENTON J D M-W C, Toutanova L K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the NAACL,.Minneapolis, Minnesota, CA, 4171-4186.https://doi.org/10.48550/arXiv.1810.04805
[10]
Hofmann T. 1999. Probabilistic latent semantic analysis. Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. [S.l.]: Morgan Kaufmann Publishers Inc, CA. 289–296. https://doi.org/10.48550/arXiv.1301.6705
[11]
Blei D M, Ng A Y,2003. Jordan M I. Latent dirichlet allocation. Journal of machine Learning research, ACM 3,3 (March 2003): 993–1022. https://dl.acm.org/doi/10.5555/944919.944937
[12]
Blei, D. M., & Lafferty, J. D. 2006. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, CA, 113–120. https://doi.org/10.5555/944919.944937
[13]
Zenodo,Grootendorst M. 2022 BERTopic: Neural topic modeling with a class -based TF-IDF procedure . ACM 11, 3 (Mar 2022), https://doi.org/10.48550/arXiv.2203.05794
[14]
McInnes L, Healy J, Melville J. UMAP. 2018. Uniform manifold approximation and projection for dimension reduction. ACM 9. 1.(February 2018), https://doi.org/10.48550/arXiv.1802.03426
[15]
McInnes L, Healy J, Astels S. hdbscan: 2017. Hierarchical density based clustering. The Journal of Open Source Software, ACM 21, 3 (March 2017), 205.https://joss.theoj.org/papers/10.21105/joss.00205
[16]
Carbinell J, Goldstein J. 1998. The use of MMR, diversity - based reranking for reordering documents and producing summaries . ACM SIGIR Forum, ACM,1, 8 (August 1998) 335-336. https://doi.org/10.1145/290941.291025
Index Terms
- Analyzing the 'Belt and Road' topic of overseas Chinese media based on the BERTopic modeling
Recommendations
Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon
User-generated reviews on the Web reflect users' sentiment about products, services and social events. Existing researches mostly focus on the sentiment classification of the product and service reviews in document level. Reviews of social events such ...
Modeling topic hierarchies with the recursive chinese restaurant process
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementTopic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that ...
Extractive text summarization using clustering-based topic modeling
AbstractText summarization is the process of converting the input document into a short form, provided that it preserves the overall meaning associated with it. Primarily, text summarization is achieved in two ways, i.e., abstractive and extractive. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
563 pages
ISBN:9798400708688
DOI:10.1145/3638584
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSAI 2023
CSAI 2023: 2023 7th International Conference on Computer Science and Artificial Intelligence
December 8 - 10, 2023
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 66Total Downloads
- Downloads (Last 12 months)66
- Downloads (Last 6 weeks)7
Reflects downloads up to 11 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format