n-stage Latent Dirichlet Allocation: A Novel Approach for LDA
arXiv:2110.08591 · doi:10.1109/UBMK.2019.8907050
Abstract
Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a text document. Latent Dirichlet Allocation (LDA) is the most common method among topic modeling methods. In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail. The positive effect of the method has been demonstrated by the applied English and Turkish studies. Since the method focuses on reducing the word count in the dictionary, it can be used language-independently. You can access the open-source code of the method and the example: https://github.com/anil1055/n-stage_LDA
arXiv commentPublished in: 2019 4th International Conference on Computer Science and Engineering (UBMK). This study is extension version of "Comparison of Topic Modeling Methods for Type Detection of Turkish News" http://dx.doi.org/10.1109/UBMK.2019.8907050 . Please citation this IEEE paper
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