Ontology‐supported polarity mining

Ontology‐supported polarity mining

Source :

• Journal of the American Society for Information Science and Technology [ 1532-2882 ] ; 2008-01-01.

English descriptors

• Teeft :
  • Aaai spring symposium, American society, Baseline, Baseline method, Building language models, Bulletin boards, Business organizations, Buvac stone, Coder, Computational, Computational approaches, Computational linguistics, Consumer opinions, Content analysis, Contextual, Contextual polarity, Contextual valence shifters, Data mining, Descriptive statistics, Discourse analysis, Empirical methods, Entire reviews, Future directions, General inquirer, General inquiry technique, Genre, Hatzivassiloglou, Hatzivassiloglou mckeown, Heuristic, High opposition scores, Hybrid approach, Imdb, Individual properties, Information management, Information retrieval, Information science, Information systems, International conference, July, Kennedy inkpen, Knowledge discovery, Language model, Language modeling, Language modeling technique, Language modeling techniques, Language models, Lexicon, Markov blanket, Maximum entropy, Mining, Modeling, Movie names, Movie review, Movie review domain, Movie review mining, Movie review ontology, Movie review properties, Movie reviews, Natural language processing, Ndings, Negative categories, Negative orientations, Negative review, Negative reviews, Next section, Nigam hurst, Ontology, Ontology concept, Ontology development, Ontology support, Opinion mining, Original version, Ospm, Ospm approach, Other methods, Other properties, Overall polarity, Palo alto, Pang, Polarity, Polarity mining, Polarity mining research, Polarity mining techniques, Polarity value, Popescu etzioni, Positive accuracy, Positive comments, Positive movie reviews, Positive reviews, Practical implications, Preliminary ontology, Product reviews, Production status, Relative rankings, Search engines, Segmented version, Semantic orientation, Semantic orientations, Sentiment analysis, Separate language model, Soundtrack, Speech recognition, Stanford university, Storytelling, Subjective expressions, Subjectivity, Subjectivity analysis, Subjectivity mining, Superior performance, Support polarity mining, System sciences, Test data, Teufel moens, Text features, Text segments, Textual, Textual documents, Textual statements, Topic mining, Traditional text mining, Training data, Turney, Turney littman, Unsupervised, Unsupervised approach, Unsupervised technique, Unsupervised techniques, Wide range, Wiebe.

Abstract

• https://api.istex.fr/document/3C25C95469DDE2B6693B53EF9894C19ABF9D8BE7/fulltext/pdf

Affiliations:

• États-Unis
• Maryland

Links toward previous steps (curation, corpus...)

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• to stream Main, to step Curation: 000707

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