The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Latent dirichlet allocation: stability and applications to studies of user-generated content
Abstract
Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of vocabulary reduction. We show the limitations of the LDA approach for the purposes of qualitative analysis in social science and sketch some ways for improvement.
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Evaluation methods for topic models
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Unsupervised Domain Clusters in Pretrained Language Models
Aharoni, R., & Goldberg, Y. (2020) · arXiv · doi:10.48550/arxiv.2004.02105 · Cited by 187
Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data
Wang, Y., Zhou, X., Noulas, A., Mascolo, C., Xie, X., & Chen, E. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/497 · Cited by 22
Metadata
- Authors
- Koltcov, S., Koltsova, O., & Nikolenko, S.
- Venue
- Proceedings of the 2014 ACM conference on Web science
- Publisher
- ACM
- Published
- 2014-06-23
- Pages
- 161-165
- OpenAlex
- W1983719983
- Cited by
- 280
- References
- 14
- FWCI
- 39.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Koltcov, S., Koltsova, O., & Nikolenko, S. (2014). Latent dirichlet allocation: stability and applications to studies of user-generated content. Proceedings of the 2014 ACM conference on Web science. https://doi.org/10.1145/2615569.2615680
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