Journal
2015 30TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE)
Volume -, Issue -, Pages 760-770Publisher
IEEE
DOI: 10.1109/ASE.2015.57
Keywords
Review Summarization; User Feedback; Sentiment Analysis; Data Mining
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Recently, Begel et al. found that one of the most important questions software developers ask is what parts of software are used/loved by users. User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects in sentences which include evaluation of aspect using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on 17 popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an average F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.
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