BMC Bioinformatics· 2015Q1
A heuristic approach to determine an appropriate number of topics in topic modeling
- 393citations
- Q1SCImago
- 2015year
Short summary
A new heuristic method using the rate of perplexity change (RPC) effectively estimates the optimal number of topics in Latent Dirichlet Allocation (LDA) models, reducing the need for extensive manual evaluation.
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Key points
- A heuristic method based on the rate of perplexity change (RPC) is proposed to determine the optimal number of topics in LDA models.
- The RPC method offers a more efficient alternative to subjective evaluations and extensive sensitivity studies.
- The method was successfully tested on three distinct datasets: Salmonella sequencing, pharmacological side effects, and computational biology abstracts.
- The approach proved stable and effective across datasets of varying types and sizes.
AI-generated from the title and abstract; the full text is not read.
Abstract
BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics data represent words. Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model development can be arduous and tedious, and requires burdensome and systematic sensitivity studies in order to find the best set of model parameters. Often, time-consuming subjective evaluations are needed to compare models. Currently, research has yielded no easy way to choose the proper number of topics in a model beyond a major iterative approach. METHODS AND RESULTS: Based on analysis of variation of statistical perplexity during topic modelling, a heuristic approach is proposed in this study to estimate the most appropriate number of topics. Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector. We test the stability and effectiveness of the proposed method for three markedly different types of grounded-truth datasets: Salmonella next generation sequencing, pharmacological side effects, and textual abstracts on computational biology and bioinformatics (TCBB) from PubMed. CONCLUSION: The proposed RPC-based method is demonstrated to choose the best number of topics in three numerical experiments of widely different data types, and for databases of very different sizes. The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter. We understand that additional investigation is needed to substantiate the method's theoretical basis, and to establish its generalizability in terms of dataset characteristics.
The authors' abstract, as published at the source. BMC Bioinformatics, 2015 · DOI ↗
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Field: General Social Sciences
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