000 05390nam a22005535i 4500
001 978-1-4020-4102-0
003 DE-He213
005 20201213200453.0
007 cr nn 008mamaa
008 100301s2006 ne | s |||| 0|eng d
020 _a9781402041020
_9978-1-4020-4102-0
024 7 _a10.1007/1-4020-4102-0
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aCOM014000
_2bisacsh
082 0 4 _a004
_223
100 1 _aShanahan, James G.
_eeditor.
245 1 0 _aComputing Attitude and Affect in Text: Theory and Applications
_h[electronic resource] /
_cedited by James G. Shanahan, Yan Qu, Janyce Wiebe.
264 1 _aDordrecht :
_bSpringer Netherlands,
_c2006.
300 _aXVI, 341p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Information Retrieval Series,
_x1387-5264 ;
_v20
505 0 _aContextual Valence Shifters -- Conveying Attitude with Reported Speech -- Where Attitudinal Expressions Get their Attitude -- Analysis of Linguistic Features Associated with Point of View for Generating Stylistically Appropriate Text -- The Subjectivity of Lexical Cohesion in Text -- A Weighted Referential Activity Dictionary -- Certainty Identification in Texts: Categorization Model and Manual Tagging Results -- Evaluating an Opinion Annotation Scheme Using a New Multi-Perspective Question and Answer Corpus -- Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes -- A Computational Semantic Lexicon of French Verbs of Emotion -- Extracting Opinion Propositions and Opinion Holders using Syntactic and Lexical Cues -- Approaches for Automatically Tagging Affect: Steps Toward an Effective and Efficient Tool -- Argumentative Zoning for Improved Citation Indexing -- Politeness and Bias in Dialogue Summarization: Two Exploratory Studies -- Generating More-Positive and More-Negative Text -- Identifying Interpersonal Distance using Systemic Features -- Corpus-Based Study of Scientific Methodology: Comparing the Historical and Experimental Sciences -- Argumentative Zoning Applied to Critiquing Novices’ Scientific Abstracts -- Using Hedges to Classify Citations in Scientific Articles -- Towards a Robust Metric of Polarity -- Characterizing Buzz and Sentiment in Internet Sources: Linguistic Summaries and Predictive Behaviors -- Good News or Bad News? Let the Market Decide -- Opinion Polarity Identification of Movie Reviews -- Multi-Document Viewpoint Summarization Focused on Facts, Opinion and Knowledge.
520 _aHuman 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.
650 0 _aComputer science.
650 0 _aInformation systems.
650 0 _aArtificial intelligence.
650 0 _aTranslators (Computer programs).
650 0 _aComputational linguistics.
650 1 4 _aComputer Science.
650 2 4 _aComputer Science, general.
650 2 4 _aInformation Systems Applications (incl.Internet).
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aLanguage Translation and Linguistics.
650 2 4 _aComputer Applications.
650 2 4 _aComputational Linguistics.
700 1 _aQu, Yan.
_eeditor.
700 1 _aWiebe, Janyce.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781402040269
830 0 _aThe Information Retrieval Series,
_x1387-5264 ;
_v20
856 4 0 _uhttp://dx.doi.org/10.1007/1-4020-4102-0
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c12572
_d12572