9/28/2023 0 Comments Topic coherence score![]() In: Canadian Conference on Artificial Intelligence, Springer, pp 215–226 (2014) H., Inkpen, D.: Text representation using multi-level latent dirichlet allocation. In: Pacific–Asia Conference on Knowledge Discovery and Data Mining, Springer, pp 256–267 (2013) Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models, In: Fourth International AAAI Conference on Weblogs and social media (2010)ĭai, Z., Sun, A., Liu, X.-Y.: Crest: cluster-based representation enrichment for short text classification. In: Advances in information retrieval, pp. Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing Twitter and traditional media using topic models. Qiang, J., Qian, Z., Li, Y., Yuan, Y., Wu, X.: Short text topic modeling techniques, applications, and performance: a survey. Vayansky, I., Kumar, S.A.P.: A review of topic modeling methods. The proposed multi-level model provides better quality of topics extracted.ĭeerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. The evaluation carried out shows that the proposed LDA-Gibbs had a coherence score of 0.52650 as against the LDA coherence score of 0.46504. To verify the effectiveness of the suggested model, an unstructured dataset was taken from a public repository. To improve the quality of topics extracted, this paper developed a hybrid-based semantic similarity measure for topic modeling combining LDA and Gibbs sampling to maximize the coherence score. However, Gibbs sampling operates on a word-by-word basis, which allows it to be used on documents with a variety of topics and modifies the topic assignment of a single word. ![]() One of the main problems of LDA is that the topics extracted are of poor quality if the document does not coherently belong to a single topic. The latent Dirichlet allocation (LDA) technique is frequently used to extract topics from pre-processed materials based on word frequency. Automatically extracting topics from large amounts of text is one of the main uses of natural language processing (NLP). ![]()
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