What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering) (1608.08176v4)

Published 29 Aug 2016 in cs.SE, cs.AI, cs.CL, and cs.IR

Abstract: Context: Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeler is Latent Dirichlet allocation. When run on different datasets, LDA suffers from "order effects" i.e. different topics are generated if the order of training data is shuffled. Such order effects introduce a systematic error for any study. This error can relate to misleading results;specifically, inaccurate topic descriptions and a reduction in the efficacy of text mining classification results. Objective: To provide a method in which distributions generated by LDA are more stable and can be used for further analysis. Method: We use LDADE, a search-based software engineering tool that tunes LDA's parameters using DE (Differential Evolution). LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands ofSoftware Engineering (SE) papers, and software defect reports from NASA. Results were collected across different implementations of LDA (Python+Scikit-Learn, Scala+Spark); across different platforms (Linux, Macintosh) and for different kinds of LDAs (VEM,or using Gibbs sampling). Results were scored via topic stability and text mining classification accuracy. Results: In all treatments: (i) standard LDA exhibits very large topic instability; (ii) LDADE's tunings dramatically reduce cluster instability; (iii) LDADE also leads to improved performances for supervised as well as unsupervised learning. Conclusion: Due to topic instability, using standard LDA with its "off-the-shelf" settings should now be depreciated. Also, in future, we should require SE papers that use LDA to test and (if needed) mitigate LDA topic instability. Finally, LDADE is a candidate technology for effectively and efficiently reducing that instability.

Authors (3)

Amritanshu Agrawal (14 papers)
Wei Fu (59 papers)
Tim Menzies (128 papers)

Citations (198)

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Summary

An Essay on "What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)"

The paper "What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)" by Agrawal, Fu, and Menzies addresses a significant limitation in the use of Latent Dirichlet Allocation (LDA) for topic modeling, particularly concerning the instability of results due to input order effects. This essay summarizes the authors' investigation into this issue, their proposed solution, and the implications for the field of software analytics.

The authors focus on a well-known limitation of LDA: its susceptibility to "order effects." These occur when the order of training data affects the LDA's output, resulting in varying topic distributions on different runs with the same dataset. This inconsistency poses a systematic error, potentially skewing results in studies that rely on LDA for topic-based analysis, such as software engineering (SE) analytics and text mining.

To mitigate these order effects, the authors introduce LDADE, a tool combining LDA with Differential Evolution (DE) to optimize LDA parameters, thereby enhancing stability. LDADE is tested against data from multiple sources, including Stackoverflow, SE research papers, and NASA defect reports, with results indicating substantial improvements in topic similarity stability and accuracy of text mining classification tasks. The research demonstrates that standard "off-the-shelf" LDA parameters frequently result in instability, which can be greatly mitigated through targeted tuning using LDADE.

A key finding of this work is the critical importance of contextual parameter tuning for each dataset. The authors present evidence that different datasets require unique configurations of LDA parameters to achieve stable and reliable outputs. The paper reveals that the "default" settings are insufficient and can lead to unreliable topic models, stressing the necessity of a bespoke approach for each unique data context.

The improvements secured by LDADE are not without their computational costs. The authors acknowledge that tuning with DE may increase the runtime of LDA three to fivefold. However, they argue that this increase is justifiable within modern computational environments, especially considering the enhanced reliability and utility of the results. Furthermore, LDADE's tuning is computationally efficient compared to other methods like genetic algorithms, offering faster and more stable outcomes.

In terms of practical and theoretical implications, the paper emphasizes the need for SE studies using LDA to incorporate tuning procedures to avoid unreliable analyses due to topic instability. The paper invites researchers to rigorously evaluate and report the stability of their LDA-based findings, potentially transforming practices in the community concerning the use of LDA for topic modeling.

Future research avenues include extending LDADE's applicability across broader types of datasets and further refining the efficiency of parameter tuning processes. The implications of order effects and the benefits of evolutionary optimization could also be explored in other unsupervised learning contexts beyond LDA, providing a rich field for further scientific investigation.

In conclusion, this paper offers a substantive examination of the limitations of topic modeling via LDA and proposes an apt solution to enhance stability and consequently the utility of LDA in software engineering and analytics at large. The findings underscore a shift towards more nuanced and dataset-specific applications of LDA, stressing the role of tuning in achieving reliable outcomes. As such, LDADE presents a viable way forward for researchers and practitioners dealing with unstructured textual data.

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