Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)

2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2015-05-18 DOI:10.1109/ASE.2015.85

P. Vu, Tam The Nguyen, H. Pham, T. Nguyen

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引用次数: 154

Abstract

User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.

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基于关键词的手机应用评论用户意见挖掘方法(T)

手机应用的用户评论通常包含抱怨或建议，这对应用开发者改善用户体验和满意度很有价值。然而，由于这些评论的大量和嘈杂的性质，手动分析它们以获得有用的意见本质上是具有挑战性的。为了解决这个问题，我们提出了MARK，一个基于关键字的半自动化评审分析框架。MARK允许分析师通过一组关键字描述他对一个或几个移动应用程序的兴趣。然后，它会找到并列出与这些关键字最相关的评论，以供进一步分析。它还可以绘制这些关键字随时间的趋势，并检测它们的突然变化，这可能表明严重问题的发生。为了帮助分析师更有效地描述他们的兴趣，MARK可以自动从原始评论中提取关键字，并根据它们与负面评论的关联对它们进行排名。此外，基于关键词的向量语义表示，MARK可以将大量关键词划分为更有凝聚力的子集，或者建议与所选关键词相似的关键词。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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