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Mining User Opinions To Support Requirement Engineering: An Empirical Study

Dąbrowski, J; Letier, E; Perini, A; Susi, A; (2020) Mining User Opinions To Support Requirement Engineering: An Empirical Study. In: International Conference on Advanced Information Systems Engineering CAiSE 2020: Advanced Information Systems Engineering. (pp. pp. 401-416). Springer, Cham: Grenoble, France. Green open access

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Abstract

App reviews provide a rich source of user opinions that can support requirement engineering activities. Analysing them manually to find these opinions, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for so-called opinion mining. These approaches facilitate the analysis by extracting features discussed in app reviews and identifying their associated sentiments. The effectiveness of these approaches has been evaluated using different methods and datasets. Unfortunately, replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present an empirical study in which, we evaluated feature extraction and sentiment analysis approaches on the same dataset. The results show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.

Type: Proceedings paper
Title: Mining User Opinions To Support Requirement Engineering: An Empirical Study
Event: 32nd International Conference on Advanced Information Systems Engineering (CAiSE 2020)
Location: Grenoble, France
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-49435-3_25
Publisher version: https://doi.org/12127
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Mining user reviews, Requirement engineering, Feature extraction, Sentiment analysis, Empirical study
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10095726
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