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by James G. Shanahan,Yan Qu,Janyce Wiebe
Download Computing Attitude and Affect in Text: Theory and Applications (The Information Retrieval Series) fb2
Computer Science
  • Author:
    James G. Shanahan,Yan Qu,Janyce Wiebe
  • ISBN:
    1402040261
  • ISBN13:
    978-1402040269
  • Genre:
  • Publisher:
    Springer; 2006 edition (January 9, 2006)
  • Pages:
    341 pages
  • Subcategory:
    Computer Science
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Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos.

Series: The Information Retrieval Series (Book 20).

ISBN-13: 978-1402040269. Whether the reader is more interested in the computational or the linguistic aspects of the problem-or even just the range of possible applications-this collection will broaden the perspective on the issue. For readers with no background in sentiment detection the volume can serve as an initial overview of the field. Series: The Information Retrieval Series (Book 20).

Электронная книга "Computing Attitude and Affect in Text: Theory and Applications", James G. Shanahan, Yan Qu, Janyce Wiebe. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Computing Attitude and Affect in Text: Theory and Applications" для чтения в офлайн-режиме.

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 The chapters in this book address attitude, affect, and subjective opinion.

Дата издания: 2005 Серия: The Information Retrieval Series Язык .

Дата издания: 2005 Серия: The Information Retrieval Series Язык: ENG Иллюстрации: Biography Размер: 2. 9 x 1. 0 x . 1 cm Читательская аудитория: General (us: trade) Рейтинг . This book uncovers such applications, explaining the underlying technology and its implementation. It demonstrates how they can be modeled, designed, and implemented. Описание: The book covers the theory and application of soft computing techniques; namely neural networks, fuzzy logic, evolutionary computing and complex systems.

Computing Attitude and Affect in Text: Theory and Applications. James G. The Information Retrieval Series. Other aspects, including pragmatics, opinion, an. More).

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Anthology ID: J07-2010. Volume: Computational Linguistics, Volume 33, Number 2, June 2007.

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.