Sentic Computing [electronic resource] :Techniques, Tools, and Applications / by Erik Cambria, Amir Hussain.
by Cambria, Erik [author.]; Hussain, Amir [author.]; SpringerLink (Online service).
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BookSeries: SpringerBriefs in Cognitive Computation: 2Publisher: Dordrecht : Springer Netherlands : 2012.Description: XVIII, 153 p. 39 illus., 35 illus. in color. online resource.ISBN: 9789400750708.Subject(s): Medicine | Data mining | Mathematics | Consciousness | Biomedicine | Biomedicine general | Data Mining and Knowledge Discovery | Mathematics, general | Linguistics (general) | Cognitive PsychologyDDC classification: 610 Online resources: Click here to access online
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Springer eBooksSummary: In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
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In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
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