n-stage Latent Dirichlet Allocation: A Novel Approach for LDA
This is an incremental improvement for natural language processing researchers and practitioners dealing with topic modeling on large datasets.
The paper tackles the problem of handling large textual data by proposing an n-stage LDA method to improve the effectiveness of Latent Dirichlet Allocation in topic modeling, with positive results demonstrated in English and Turkish studies.
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