University of Oulu

Mika V. Mantyla, Maelick Claes, and Umar Farooq. 2018. Measuring LDA topic stability from clusters of replicated runs. In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '18). ACM, New York, NY, USA, Article 49, 4 pages. DOI: https://doi.org/10.1145/3239235.3267435

Measuring LDA topic stability from clusters of replicated runs

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Author: Mäntylä, Mika V.1; Claes, Maëlick1; Farooq, Umar1
Organizations: 1M3S, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901031265
Language: English
Published: Association for Computing Machinery, 2018
Publish Date: 2019-01-03
Description:

Abstract

Background: Unstructured and textual data is increasing rapidly and Latent Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods for it. Past work suggests that instability of LDA topics may lead to systematic errors.

Aim: We propose a method that relies on replicated LDA runs, clustering, and providing a stability metric for the topics.

Method: We generate k LDA topics and replicate this process n times resulting in n*k topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k clusters now represent the original LDA topics and we present them like normal LDA topics showing the ten most probable words. For the clusters, we try multiple stability metrics, out of which we recommend Rank-Biased Overlap, showing the stability of the topics inside the clusters.

Results: We provide an initial validation where our method is used for 270,000 Mozilla Firefox commit messages with k=20 and n=20. We show how our topic stability metrics are related to the contents of the topics.

Conclusions: Advances in text mining enable us to analyze large masses of text in software engineering but non-deterministic algorithms, such as LDA, may lead to unreplicable conclusions. Our approach makes LDA stability transparent and is also complementary rather than alternative to many prior works that focus on LDA parameter tuning.

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ISBN: 978-1-4503-5823-1
Article number: 49
DOI: 10.1145/3239235.3267435
OADOI: https://oadoi.org/10.1145/3239235.3267435
Host publication: ESEM '18 Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Oulu, Finland, October 11-12, 2018
Conference: International symposium on empirical software engineering and measurement
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work has been supported by Academy of Finland grant 298020.
Academy of Finland Grant Number: 298020
Detailed Information: 298020 (Academy of Finland Funding decision)
Copyright information: © ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '18). ACM, New York, NY, USA, Article 49, 4 pages. DOI: https://doi.org/10.1145/3239235.3267435.