| Computing Attitude and Affect in Text: Theory and Applications 2006 Edition Contributor(s): Shanahan, James G. (Editor), Qu, Yan (Editor), Wiebe, Janyce (Editor) |
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ISBN: 1402040261 ISBN-13: 9781402040269 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: November 2005 Annotation: 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 attitude, affect, and subjective opinion. Various conceptual models and computational methods are presented, including 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, such as indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups; analyzing client discourse in therapy and counseling; determining relations between scientific texts; generating more appropriate texts; and creating writers? aids. In addition to English texts, the collection includes studies of French, Japanese, and Portuguese texts. The chapters in this book are extended and revised versions of papers presented at the American Association for Artificial Intelligence (AAAI) Spring Symposium on Exploring Attitude and Affect in Text, which took place in March 2004 at Stanford University. The symposium, and the book which grew out it, represents a first foray into this area and a balance among conceptual models, computational methods, and applications. |
| Additional Information |
| BISAC Categories: - Computers | Information Technology - Language Arts & Disciplines | Library & Information Science - General - Computers | System Administration - Storage & Retrieval |
| Dewey: 025.524 |
| LCCN: 2006296724 |
| Series: Information Retrieval |
| Physical Information: 0.77" H x 6.53" W x 9.72" (1.69 lbs) 341 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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 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. |
