Abstract
Latent Dirichlet Allocation (LDA) is a topic model that represents a document as a distribution of multiple topics. It expresses each topic as a distribution of multiple words by mining semantic relationships hidden in text. However, traditional LDA ignores some of the semantic features hidden inside the document semantic structure of medium and long texts. Instead of using the original LDA to model the topic at the document level, it is better to refine the document into different semantic topic units. In this paper, we propose an improved LDA topic model based on partition (LDAP) for medium and long texts. LDAP not only preserves the benefits of the original LDA but also refines the modeled granularity from the document level to the semantic topic level, which is particularly suitable for the topic modeling of the medium and long text. The extensive experimental classification results on Fudan University corpus and Sougou Lab corpus demonstrate that LDAP achieves better performance compared with other topic models, such as LDA, HDP, LSA and doc2vec.
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Acknowledgements
This research was supported in part by the National Natural Science Foundation of China [Grant Nos. 71771034, 71421001], Science and Technology Program of Jieyang (2017xm041), and the Scientific and Technological Innovation Foundation of Dalian (2018J11CY009).
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Guo, C., Lu, M. & Wei, W. An Improved LDA Topic Modeling Method Based on Partition for Medium and Long Texts. Ann. Data. Sci. 8, 331–344 (2021). https://doi.org/10.1007/s40745-019-00218-3
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DOI: https://doi.org/10.1007/s40745-019-00218-3