Computing Attitude and Affect in Text: Theory and Applications

  • James G. Shanahan
  • Yan Qu
  • Janyce Wiebe

Part of the The Information Retrieval Series book series (INRE, volume 20)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Livia Polanyi, Annie Zaenen
    Pages 1-10
  3. Sabine Bergler
    Pages 11-22
  4. Jussi Karlgren, Gunnar Eriksson, Kristofer Franzén
    Pages 23-31
  5. Jane Morris, Graeme Hirst
    Pages 41-47
  6. Wilma Bucci, Bernard Maskit
    Pages 49-60
  7. Victoria L. Rubin, Elizabeth D. Liddy, Noriko Kando
    Pages 61-76
  8. Veselin Stoyanov, Claire Cardie, Diane Litman, Janyce Wiebe
    Pages 77-91
  9. Steven Bethard, Hong Yu, Ashley Thornton, Vasileios Hatzivassiloglou, Dan Jurafsky
    Pages 125-141
  10. Nathanael Chambers, Joel Tetreault, James Allen
    Pages 143-158
  11. Norton Trevisan Roman, Paul Piwek, Ariadne Maria Brito Rizzoni Carvalho
    Pages 171-185
  12. Diana Zaiu Inkpen, Ol’ga Feiguina, Graeme Hirst
    Pages 187-198
  13. Casey Whitelaw, Jon Patrick, Maria Herke-Couchman
    Pages 199-214
  14. Valéria D. Feltrim, Simone Teufel, Maria Graças V. das Nunes, Sandra M. Aluísio
    Pages 233-246
  15. Chrysanne Di Marco, Frederick W. Kroon, Robert E. Mercer
    Pages 247-263

About this book

Introduction

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.

Keywords

Text artificial intelligence berck corpus intelligence natural language processing therapy

Editors and affiliations

  • James G. Shanahan
    • 1
  • Yan Qu
    • 1
  • Janyce Wiebe
    • 2
  1. 1.Clairvoyance CooperationPittsburghUSA
  2. 2.University of PittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/1-4020-4102-0
  • Copyright Information Springer 2006
  • Publisher Name Springer, Dordrecht
  • eBook Packages Computer Science
  • Print ISBN 978-1-4020-4026-9
  • Online ISBN 978-1-4020-4102-0
  • Series Print ISSN 1387-5264
  • About this book