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Detecting Opinions and their Opinion Targets
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/07-NTCIR8-MOAT-ChoiY.pdf (2010)
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Domain-specific sentiment analysis using contextual feature generation
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In: http://ir.kaist.ac.kr/papers/2009/tsa09.pdf (2009)
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Abstract:
This paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more contextual-information such as topic or domain is essential. In our system, we first automatically extract sentiment clues in different domains by our observation. We identified that a sentiment clue is often syntactically related to a sentiment topic in a sentence, which is defined as a primary subject of sentiment expression, such as event, company, and person. We bootstrap from a small set of seed clues and generate new clues by utilizing linguistic dependencies and collocation information between sentiment clues and sentiment topics. Next, we learn a domain-specific sentiment classifier for each domain with the newly aggregated clues. We ran experiments to see how the bootstrapping algorithm to converge and aggregate new clues and verified that the extracted domain-context features are more effective than generally-used features in sentiment analysis by running them on the same sentiment classifier.
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Keyword:
Algorithms; Experimentation. Keywords Sentiment
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URL: http://ir.kaist.ac.kr/papers/2009/tsa09.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.7350
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Opinion Analysis based on Lexical Clues and their Expansion
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/53.pdf (2007)
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Opinion Analysis based on Lexical Clues and their Expansion
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In: http://ir.kaist.ac.kr/papers/2007/yhkim_opinion_final - NTCIR.pdf (2007)
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Automatic Identification of Text Genres and Their Roles in SubjectBased Categorization
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In: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf (2004)
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Overview of clir task at the third ntcir workshop
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In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2002a.pdf (2002)
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Complementing Dictionary-Based Query Translations with Corpus Statistics for Cross-Language IR
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In: http://www.mt-archive.info/MTS-1999-Myaeng.pdf (1999)
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Using Mutual Information to Resolve Query Translation
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In: http://acl.ldc.upenn.edu/P/P99/P99-1029.pdf (1999)
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Using Syntactic Dependencies and WordNet Classes for Noun Event Recognition
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In: http://ceur-ws.org/Vol-902/paper_5.pdf
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Unsupervised word sense disambiguation using
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In: http://ir.icu.ac.kr/papers/Unsupervised_Word_Sense_Disambiguation.pdf
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Detecting experiences from weblogs
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In: http://aclweb.org/anthology-new/P/P10/P10-1148.pdf
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Concept Unification of Terms in Different Languages for IR
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In: http://www.mt-archive.info/Coling-ACL-2006-Li.pdf
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Concept Unification of Terms in Different Languages for IR
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In: http://ir.kaist.ac.kr/papers/2006/Concept Unification of Terms in Different Languages for IR.pdf
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Proceedingsof the Third NTCIR Workshop Overview of CLIR Task at the Third NTCIR Workshop
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-OV-CLIR-ChenK.rev.pdf
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***Department of Statistics, Chungnam National University
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In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2005d.pdf
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Generating and Mixing Feature Sets from Language Models for Sentiment Classification
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In: http://ir.kaist.ac.kr/papers/2009/nlp-ke2009.pdf
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Automatic Discovery of Technology Trends from Patent Text
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In: http://ir.kaist.ac.kr/papers/2008/20081025_2009_SAC_Camera-ready.pdf
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Simple Query Translation Methods for Korean-English and Korean-Chinese CLIR in NTCIR Experiments
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-CLIR-JangM.pdf
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Concept Unification of Terms in Different Languages for IR
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In: http://acl.ldc.upenn.edu/P/P06/P06-1081.pdf
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Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting
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