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A heuristic approach to determine an appropriate number of topics in topic modeling.

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W - BMC Bioinformatics (2015)

Bottom Line: 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.Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector.The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter.

View Article: PubMed Central - HTML - PubMed

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.

No MeSH data available.


Four example topics derived by LDA modeling with 60 topics on TCBB dataset.
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Figure 5: Four example topics derived by LDA modeling with 60 topics on TCBB dataset.

Mentions: In the LDA models with 60 topics, a larger number of topics were judged to be less meaningful in terms of being able to discern a unique and salient theme, compared to the model with 40 topics. Figure 5 gives word cloud representations of four illustrative topics. In each, a few words are displayed with comparable large front size, indicating that these words have comparable high probabilities within the same topic. Consequently, it is hard to distinguish the theme for each topic.


A heuristic approach to determine an appropriate number of topics in topic modeling.

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W - BMC Bioinformatics (2015)

Four example topics derived by LDA modeling with 60 topics on TCBB dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597325&req=5

Figure 5: Four example topics derived by LDA modeling with 60 topics on TCBB dataset.
Mentions: In the LDA models with 60 topics, a larger number of topics were judged to be less meaningful in terms of being able to discern a unique and salient theme, compared to the model with 40 topics. Figure 5 gives word cloud representations of four illustrative topics. In each, a few words are displayed with comparable large front size, indicating that these words have comparable high probabilities within the same topic. Consequently, it is hard to distinguish the theme for each topic.

Bottom Line: 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.Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector.The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter.

View Article: PubMed Central - HTML - PubMed

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.

No MeSH data available.