情感分析在妇产科和助产学中的应用进展

Stavroula G Barbounaki, Kleanthi Gourounti, Antigoni Sarantaki
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引用次数: 7

摘要

背景:情绪分析,也称为“意见挖掘”或“情绪AI”,处理自然语言,分析文本,并使用计算语言学和生物识别技术来识别和分析情绪和主观信息。情绪分析主要应用于市场营销和客户服务等领域,也应用于临床医学。随着越来越多的研究人员在这项有价值的技术的帮助下进行研究,并注意到它在该领域的贡献,临床医学相关情绪分析最近取得了进展。目的:本综述的目的是介绍在线数据库中存放的文章中描述的情感分析的重要事实,以及经过批判性评价的相关文章和进行的叙事综合。方法:系统检索PubMed、APA PsycINFO、SCOPUS、ScienceDirect四个电子数据库。这篇综述只考虑了2006-2021年发表的与目标相关的英语定量初级研究,没有地域限制。检索词为“情绪分析”和“产科”或“妊娠”、“新冠肺炎”或“围产期”或“产后”或“胎儿”或“母乳喂养”或“宫颈”。结果和讨论:对相关文章进行了批判性评价,并进行了叙述性综合。正如大量研究表明情绪分析在临床医学领域的应用一样,它被证明是非常有用的,有助于调查一些非常重要甚至以前未探索的问题。结论:由于孕妇比以往任何时候都更公开地表达自己的想法和感受,情绪分析正成为监测和理解这种情绪的重要工具。鉴于已经提供了大量的知识情感分析,预计未来将进一步研究使用这一技术。
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Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery.

Background: Sentiment analysis, which is also referred to as 'opinion mining' or 'emotion AI', processes natural language, analyzes text and employs computational linguistics, and biometrics to identify and analyze emotions and subjective information. Sentiment analysis is mostly applied in domains such as marketing and customer service but also in clinical medicine. Clinical medicine- related sentiment analysis has advanced recently, as more and more researchers are performing studies with the help of this valuable technique, having noticed its ability to contribute in the field.

Objective: The aim of this review was to present important facts about sentimental analysis described in deposited articles in on-line databases and the relevant articles critically appraised and a narrative synthesis conducted.

Methods: A systematic search of four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect) was performed. This review considered only quantitative, primary studies in English language, without geographical limitations, published from 2006-2021 and relevant to the objective. Searching terms were 'Sentiment analysis' AND 'Obstetrics' OR 'pregnancy', OR 'COVID' OR 'Perinatal distress' OR 'postpartum period' OR 'fetal' OR 'breast feeding' OR 'cervical'.

Results and discussion: Relevant articles were critically appraised and a narrative synthesis was conducted. As a large number of studies, illustrates the use of sentiment analysis in the domain of clinical medicine, it is proved to be extremely helpful, assisting in the investigation of some highly important and even previously unexplored issues.

Conclusion: Since pregnant women express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Given the vast knowledge sentiment analysis has already offered, further studies employing this technique are expected in the future.

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