Identifying emerging trends and hot topics through intelligent data mining: the case of clinical psychology and psychotherapy

IF 2.3 Q3 REGIONAL & URBAN PLANNING
Foresight Pub Date : 2023-10-03 DOI:10.1108/fs-02-2023-0026
Anna Sokolova, Polina Lobanova, Ilya Kuzminov
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引用次数: 0

Abstract

Purpose The purpose of the paper is to present an integrated methodology for identifying trends in a particular subject area based on a combination of advanced text mining and expert methods. The authors aim to test it in an area of clinical psychology and psychotherapy in 2010–2019. Design/methodology/approach The authors demonstrate the way of applying text-mining and the Word2Vec model to identify hot topics (HT) and emerging trends (ET) in clinical psychology and psychotherapy. The analysis of 11.3 million scientific publications in the Microsoft Academic Graph database revealed the most rapidly growing clinical psychology and psychotherapy terms – those with the largest increase in the number of publications reflecting real or potential trends. Findings The proposed approach allows one to identify HT and ET for the six thematic clusters related to mental disorders, symptoms, pharmacology, psychotherapy, treatment techniques and important psychological skills. Practical implications The developed methodology allows one to see the broad picture of the most dynamic research areas in the field of clinical psychology and psychotherapy in 2010–2019. For clinicians, who are often overwhelmed by practical work, this map of the current research can help identify the areas worthy of further attention to improve the effectiveness of their clinical work. This methodology might be applied for the identification of trends in any other subject area by taking into account its specificity. Originality/value The paper demonstrates the value of the advanced text-mining approach for understanding trends in a subject area. To the best of the authors’ knowledge, for the first time, text-mining and the Word2Vec model have been applied to identifying trends in the field of clinical psychology and psychotherapy.
通过智能数据挖掘识别新兴趋势和热点话题:临床心理学和心理治疗的案例
本文的目的是提出一种基于高级文本挖掘和专家方法相结合的综合方法,用于识别特定主题领域的趋势。作者的目标是在2010-2019年在临床心理学和心理治疗领域进行测试。设计/方法/方法作者展示了如何应用文本挖掘和Word2Vec模型来识别临床心理学和心理治疗中的热点话题(HT)和新兴趋势(ET)。对微软学术图谱数据库中1130万份科学出版物的分析显示,临床心理学和心理治疗术语增长最快,这些术语的出版物数量增长最快,反映了现实或潜在的趋势。所提出的方法允许人们识别与精神障碍、症状、药理学、心理治疗、治疗技术和重要心理技能相关的六个专题集群的HT和ET。开发的方法使人们能够看到2010-2019年临床心理学和心理治疗领域最具活力的研究领域的广阔图景。对于经常被实际工作淹没的临床医生来说,这张当前研究的地图可以帮助确定值得进一步关注的领域,以提高他们临床工作的有效性。考虑到任何其他主题领域的特殊性,这种方法可用于确定其趋势。原创性/价值本文展示了高级文本挖掘方法在理解主题领域趋势方面的价值。据作者所知,文本挖掘和Word2Vec模型首次被应用于临床心理学和心理治疗领域的趋势识别。
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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来源期刊
Foresight
Foresight REGIONAL & URBAN PLANNING-
CiteScore
5.10
自引率
5.00%
发文量
45
期刊介绍: ■Social, political and economic science ■Sustainable development ■Horizon scanning ■Scientific and Technological Change and its implications for society and policy ■Management of Uncertainty, Complexity and Risk ■Foresight methodology, tools and techniques
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