# n-stage Latent Dirichlet Allocation: A Novel Approach for LDA

arXiv: https://arxiv.org/abs/2110.08591 · HTML: https://mlsanity.com/paper/2110.08591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization](https://mlsanity.com/paper/1407.7299/) (2014)
- [Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database](https://mlsanity.com/paper/1610.01520/) (2016)
- [Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints](https://mlsanity.com/paper/1807.07468/) (2018)
- [Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets](https://mlsanity.com/paper/2110.00418/) (2021)

## Abstract (arXiv)

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