书目名称 | Computing Attitude and Affect in Text: Theory and Applications | 编辑 | James G. Shanahan,Yan Qu,Janyce Wiebe | 视频video | | 概述 | A seminal collection of work on computing attitude, affect, and sentiment.A balance of conceptual models, computational models, and applications.Cross-disciplinary, catering to a broad audience.Access | 丛书名称 | The Information Retrieval Series | 图书封面 |  | 描述 | 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 author’s reports from reports of other people’s 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 | 出版日期 | Book 2006 | 关键词 | Text; artificial intelligence; berck; corpus; intelligence; natural language processing; therapy | 版次 | 1 | doi | https://doi.org/10.1007/1-4020-4102-0 | isbn_softcover | 978-94-007-9257-9 | isbn_ebook | 978-1-4020-4102-0Series ISSN 1871-7500 Series E-ISSN 2730-6836 | issn_series | 1871-7500 | copyright | Springer Science+Business Media B.V. 2006 |
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