Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
Cambria, Erik
Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis by Erik Cambria, Amir Hussain. - 1st ed. 2015. - 1 recurso en línea (XXII, 176 páginas) 54 ilustraciones, 40 ilustraciones en color - Socio-Affective Computing 1 .
Introduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.
This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: â€� Âtic Computing's multi-disciplinary approach to sentimentÂanalysis-evidenced by concomitant use of AI, linguistics and psychology for knowledge representation and inference â€� Âtic Computingâ€hift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text â€� Âtic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited by Amir HussainÂd Dr Erik Cambria will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.
9783319236544
10.1007/978-3-319-23654-4 doi
Semántica--Proceso de datos
Inteligencia artificial
QA76.5913 / 2015 EB
Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis by Erik Cambria, Amir Hussain. - 1st ed. 2015. - 1 recurso en línea (XXII, 176 páginas) 54 ilustraciones, 40 ilustraciones en color - Socio-Affective Computing 1 .
Introduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.
This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: â€� Âtic Computing's multi-disciplinary approach to sentimentÂanalysis-evidenced by concomitant use of AI, linguistics and psychology for knowledge representation and inference â€� Âtic Computingâ€hift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text â€� Âtic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited by Amir HussainÂd Dr Erik Cambria will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.
9783319236544
10.1007/978-3-319-23654-4 doi
Semántica--Proceso de datos
Inteligencia artificial
QA76.5913 / 2015 EB