Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models
Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models
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DOI：
10.1145/3510003.3510201
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Miroslav Tushev;Fahimeh Ebrahimi;Anas Mahmoud
中科院分区：
文献类型：
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作者：
Miroslav Tushev;Fahimeh Ebrahimi;Anas Mahmoud
移动应用程序 (app) 评论包含对应用程序开发人员有价值的信息。文献中已经提出了大量的监督和非监督技术来从应用程序评论中综合有用的用户反馈。然而，传统的监督分类算法需要大量的手动工作来标记真实数据，而无监督文本挖掘技术（例如主题模型）由于评论中有用信息的稀疏性，通常会产生次优结果。为了克服这些限制，在本文中，我们提出了一种全自动且无监督的方法，用于从移动应用程序评论中提取有用信息。所提出的方法基于 keyATM，这是一种用于生成主题模型的关键字辅助方法。 keyATM 通过使用直接从评论语料库中提取的种子关键词来克服数据稀疏的问题。然后使用这些关键字生成有意义的特定领域主题。我们的方法通过从投资和食品配送应用程序领域采样的两个移动应用程序评论数据集进行评估。结果表明，与传统主题建模技术相比，我们的方法产生的主题明显更加连贯。
Mobile application (app) reviews contain valuable information for app developers. A plethora of supervised and unsupervised techniques have been proposed in the literature to synthesize useful user feedback from app reviews. However, traditional supervised classification algorithms require extensive manual effort to label ground truth data, while unsupervised text mining techniques, such as topic models, often produce suboptimal results due to the sparsity of useful information in the reviews. To overcome these limitations, in this paper, we propose a fully automatic and unsupervised approach for extracting useful information from mobile app reviews. The proposed approach is based on keyATM, a keyword-assisted approach for generating topic models. keyATM overcomes the prob-lem of data sparsity by using seeding keywords extracted directly from the review corpus. These keywords are then used to generate meaningful domain-specific topics. Our approach is evaluated over two datasets of mobile app reviews sampled from the domains of Investing and Food Delivery apps. The results show that our approach produces significantly more coherent topics than traditional topic modeling techniques.