Keyword-centered Collocating Topic Analysis

Yu-Lin Chang, Yongfu Liao, Po-Ya Angela Wang, Mao-Chang Ku, Shu-Kai Hsieh

Abstract
The rapid flow of information and the abundance of text data on the Internet have brought about the urgent demand for the construction of monitoring resources and techniques used for various purposes. To extract facets of information useful for particular domains from such large and dynamically growing corpora requires an unsupervised yet transparent ways of analyzing the textual data. This paper proposed a hybrid collocation analysis as a potential method to retrieve and summarize Taiwan-related topics posted on Weibo and PTT. By grouping collocates of 臺灣 ‘Taiwan’ into clusters of topics via either word embeddings clustering or Latent Dirichlet allocation, lists of collocates can be converted to probability distributions such that distances and similarities can be defined and computed. With this method, we conduct a diachronic analysis of the similarity between Weibo and PTT, providing a way to pinpoint when and how the topic similarity between the two rises or falls. A fine-grained view on the grammatical behavior and political implications is attempted, too. This study thus sheds light on alternative explainable routes for future social media listening method on the understanding of cross-strait relationship.
Anthology ID:
2021.rocling-1.40
Volume:
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Month:
October
Year:
2021
Address:
Taoyuan, Taiwan
Editors:
Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
310–317
Language:
URL:
https://aclanthology.org/2021.rocling-1.40
DOI:
Bibkey:
Cite (ACL):
Yu-Lin Chang, Yongfu Liao, Po-Ya Angela Wang, Mao-Chang Ku, and Shu-Kai Hsieh. 2021. Keyword-centered Collocating Topic Analysis. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 310–317, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
Cite (Informal):
Keyword-centered Collocating Topic Analysis (Chang et al., ROCLING 2021)
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