分析在线客户评论的自然语言处理:一项调查,分类学和开放式研究挑战
Nadia Malik1, Muhammad Bilal2,3
1Department of Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan.
PeerJ. Computer science
|August 15, 2024
概括
本研究对分析在线客户评论的自然语言处理 (NLP) 应用进行了分类,突出了关键的方法和挑战. 研究显示,对于用于电子商务审查分析的NLP越来越感兴趣.
科学领域:
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
- 电子商务分析 电子商务分析
背景情况:
- 电子商务的增长增加了对产品评估的在线评论的依赖.
- 自然语言处理 (NLP) 对于分析大量客户反至关重要.
- 现有的文献缺乏在这个领域中NLP应用的综合分类.
研究的目的:
- 为分析在线客户评论建立NLP应用程序的全面分类.
- 审查最先进的方法,数据源和该领域的研究挑战.
- 确定新兴趋势和未来的研究方向.
主要方法:
- 从2013年到2023年,对154篇出版物的系统文献审查.
- 将NLP应用分为五个主要领域的分类.
- 对新兴技术和数据源的分析.
主要成果:
- 一种分类法,将NLP应用分为:情绪分析,评论管理,客户体验,用户配置文件和营销/声誉管理.
- 亚马逊用户评论在现有研究中的占主导地位.
- 越来越多地采用先进的NLP模型,如BERT和LSTM.
结论:
- 对于理解在线客户评论至关重要,NLP的应用涵盖了多个业务功能.
- 未来的研究应该探索多样化的数据源和先进的NLP技术.
- 该领域显示出显著的增长和越来越多的学术兴趣.
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