Sentic Computing : Techniques, Tools, and Applications

Erik Cambria

ISBN 10: 9400750692 / ISBN 13: 9789400750692

Published by Springer, Springer, 2012

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9789400750692

Bibliographic Details

• Title: Sentic Computing : Techniques, Tools, and Applications
• Publisher: Springer, Springer
• Publication Date: 2012
• Language: English
• ISBN 10: 9400750692
• ISBN 13: 9789400750692
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 10D millimeters 235 millimeters width by 155 millimeters height by 10 millimeters depth
• Weight: 271 grams

About this title

Synopsis

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.