Using frame semantics for classifying and summarizing application store reviews

Document Type

Article

Publication Date

12-1-2018

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.

Publication Source (Journal or Book title)

Empirical Software Engineering

First Page

3734

Last Page

3767

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