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.
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Notes
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“Lifestyle” category includes contents from other subcategories, e.g., health and sport.
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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.
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Acknowledgements
This work was supported by KMITL Research Fund under Research Seed Grant for New Lecturer with grant number: KREF186507.
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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
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