Abstract
The Bag-of-Words (BOW) model is simple but one of the successful representations of text documents. This model, however, suffers from the sparse matrix, in which most of the elements are zero. Topic modeling is an unsupervised learning method that can represent text documents in a low-dimensional space. Latent Dirichlet Allocation (LDA) is a topic modeling technique used for topic extraction and data exploration, with interpretable output. This paper presents a thorough study of potential benefits of applying LDA, as a feature extraction, to topic discovery and document classification in Thai news articles, comparing with TF–IDF and Word2Vec. We also studied how much of the top Thai terms extracted from LDA with the different numbers of topics can be interpretable and meaningful, and can be a representative of the corpus. Besides, a set of Topic Coherence measures were included in our study to estimate the degree of semantic similarity of extracted topics. To compare the performance and optimization time of classification of features from the different feature extraction methods, various types of classifiers, e.g., Logistic Regression, Random Forest, XGBoosting, etc., were experimented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
“Lifestyle” category includes contents from other subcategories, e.g., health and sport.
- 3.
As Accuracy is in percentage, we do not need any normalization like TL.
- 4.
We provide a hyperlink for each Thai word leading to its meaning in English.
- 5.
References
Aletras, N., Stevenson, M.: Evaluating topic coherence using distributional semantics. In: IWCS 2013 (2013)
Asawaroengchai, C., Chaisangmongkon, W., Laowattana, D.: Probabilistic learning models for topic extraction in Thai language. In: 2018 5th International Conference on Business and Industrial Research (2018)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan) (2003)
Bonaccorso, G.: Machine learning algorithms (2017)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of GSCL, vol. 30 (2009)
Chen, T., et al.: XGBoost: extreme gradient boosting. R package version 0.4-2 1(4) (2015)
Chormai, P., Prasertsom, P., Rutherford, A.: AttaCut: a fast and accurate neural Thai word segmenter (2019)
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., et al. (eds.) NeurIPS, vol. 30 (2017)
Li, C., et al.: LDA meets word2vec: a novel model for academic abstract clustering. In: WWW 2018 (2018)
Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1(4) (1957)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 26 (2013)
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: EMNLP (2011)
Nararatwong, R., Legaspi, R., Cooharojananone, N., Okada, H., Maruyama, H.: Solving the difficult problem of topic extraction in Thai tweets. J. Telecommun. Electron. Comput. Eng. 8(6) (2016)
Newman, D., Lau, J.H., Grieser, K., Baldwin, T.: Automatic evaluation of topic coherence. In: NAACL HLT 2010 (2010)
Pitichotchokphokhin, P., Chuangkrud, P., Kalakan, K., Suntisrivaraporn, B., Leelanupab, T., Kanungsukkasem, N.: Discover underlying topics in Thai news articles: a comparative study of probabilistic and matrix factorization approaches. In: ECTI-CON 2020 (2020)
Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: WSDM 2015 (2015)
Wang, Z., Ma, L., Zhang, Y.: A hybrid document feature extraction method using latent Dirichlet allocation and word2vec. In: DSC 2016 (2016)
Acknowledgements
This work was supported by KMITL Research Fund under Research Seed Grant for New Lecturer with grant number: KREF186507.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kanungsukkasem, N., Chuangkrud, P., Pitichotchokphokhin, P., Damrongrat, C., Leelanupab, T. (2023). When are Latent Topics Useful for Text Mining?. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-42430-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42429-8
Online ISBN: 978-3-031-42430-4
eBook Packages: Computer ScienceComputer Science (R0)