Book Reviews: Computing Attitude and Affect in Text: Theory and Applications, edited by James G. Shanahan, Yan Qu, and Janyce Wiebe
Book Reviews: Computing Attitude and Affect in Text: Theory and Applications, edited by James G. Shanahan, Yan Qu, and Janyce Wiebe
复制标题
DOI：
10.1162/coli.2007.33.2.275
复制
发表时间：
2007-06
影响因子：
9.3
通讯作者：
Michael Gamon
中科院分区：
文献类型：
--
作者：
Michael Gamon
人类语言技术(HLT)和自然语言处理(NLP)系统通常侧重于内容分析的事实方面。其他方面，包括语用、观点和风格，受到的关注要少得多。然而，要达到对文本的充分理解，这些方面是不能忽视的。这本书中的章节涉及主观意见的方面，包括确定不同的观点，确定不同的情感维度，以及按意见对文本进行分类。提出了各种概念模型和计算方法。本书探讨的模式包括：区分态度和简单的事实断言；区分作者的报告和其他人的意见报告；区分明确和含蓄的态度。此外，许多应用被描述为受益于理解态度和情感的能力，包括通过意见索引和检索文档；关于意见的自动问题回答；媒体和关于消费品、政治问题等的讨论组中的情绪分析；品牌和声誉管理；发现和预测消费者和投票趋势；在治疗和咨询中分析客户话语；通过寻找引用的原因来确定科学文本之间的关系；生成更合适的文本并使代理人更可信；以及创建作者辅助工具。这里报道的研究是在不同的语言上进行的，如英语、法语、日语和葡萄牙语。然而，困难的挑战依然存在。可以说，分析语篇中的态度和情感是一个自然语言处理的完全问题。
Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the factual aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented. The models explored in this book include the following: distinguishing attitudes from simple factual assertions; distinguishing between the authors reports from reports of other peoples opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, including indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups about consumer products, political issues, etc. ; brand and reputation management; discovering and predicting consumer and voting trends; analyzing client discourse in therapy and counseling; determining relations between scientific texts by finding reasons for citations; generating more appropriate texts and making agents more believable; and creating writers aids. The studies reported here are carried out on different languages such as English, French, Japanese, and Portuguese. Difficult challenges remain, however. It can be argued that analyzing attitude and affect in text is an NLP-complete problem.