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Using frame semantics for classifying and summarizing application store reviews

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Abstract

Text mining techniques have been recently employed to classify and summarize user reviews on mobile application stores. However, due to the inherently diverse and unstructured nature of user-generated online textual data, text-based review mining techniques often produce excessively complicated models that are prone to overfitting. In this paper, we propose a novel approach, based on frame semantics, for app review mining. Semantic frames help to generalize from raw text (individual words) to more abstract scenarios (contexts). This lower-dimensional representation of text is expected to enhance the predictive capabilities of review mining techniques and reduce the chances of overfitting. Specifically, our analysis in this paper is two-fold. First, we investigate the performance of semantic frames in classifying informative user reviews into various categories of actionable software maintenance requests. Second, we propose and evaluate the performance of multiple summarization algorithms in generating concise and representative summaries of informative reviews. Three different datasets of app store reviews, sampled from a broad range of application domains, are used to conduct our experimental analysis. The results show that semantic frames can enable an efficient and accurate review classification process. However, in review summarization tasks, our results show that text-based summarization generates more comprehensive summaries than frame-based summarization. Finally, we introduces MARC 2.0, a review classification and summarization suite that implements the algorithms investigated in our analysis.

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Notes

  1. https://www.statista.com/topics/1729/app-stores/

  2. https://framenet.icsi.berkeley.edu/fndrupal/

  3. Our dataset is publicly available at http://seel.cse.lsu.edu/data/emse18.zip

  4. Randomization in our analysis is implemented using the .NET Random class

  5. www.cs.waikato.ac.nz/~ml/weka/

  6. www.cs.cmu.edu/~ark/SEMAFOR/

  7. http://seel.cse.lsu.edu/data/emse18.zip

  8. http://demo.ark.cs.cmu.edu/parse

  9. https://github.com/seelprojects/MARC-2.0

  10. https://framenet.icsi.berkeley.edu/fndrupal/current_status

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Acknowledgments

This work was supported in part by the Louisiana Board of Regents Research Competitiveness Subprogram (LA BoR-RCS), contract number: LEQSF(2015-18)-RD-A-07.

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  1. Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA

    Nishant Jha & Anas Mahmoud

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  1. Nishant Jha
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Correspondence to Anas Mahmoud.

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Communicated by: Paul Grünbacher and Anna Perini

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Jha, N., Mahmoud, A. Using frame semantics for classifying and summarizing application store reviews. Empir Software Eng 23, 3734–3767 (2018). https://doi.org/10.1007/s10664-018-9605-x

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