This paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user-generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e-commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences.

中文翻译：

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在线评论中的数据和文本挖掘：自动文献分析

本文报告了使用数据和文本挖掘对科学文献进行彻底分析，以从在线评论中发现知识，因为它们作为用户生成内容的重要性。在此背景下，在过去 15 年中，从 Scopus 数据库索引的出版物中提取了 12,000 多篇论文。关于数据类型，以前的大多数研究都集中在定性文本数据上进行分析，较少寻找定量分数和/或表征审稿人资料。在应用领域方面，信息管理与技术、电子商务、旅游脱颖而出。同样清楚的是，在未来的研究中应该解决其他潜在有价值的应用领域，例如艺术和教育，以及更多的跨学科方法，即在社会科学的范围内。