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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
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Computing Attitude and Affect in Text: Theory and Applications (Paperback, 2006 ed.)
Loot Price: R4,367
Discovery Miles 43 670
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Computing Attitude and Affect in Text: Theory and Applications (Paperback, 2006 ed.)
Series: The Information Retrieval Series, 20
Expected to ship within 10 - 15 working days
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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.
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