Sentic Computing: A Common-sense-based Framework For Concept-level Sentiment Analysis (socio-affective Computing)
by Amir Hussain /
2015 / English / PDF
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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.
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.
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:
Readers will discover the following key novelties, that make this
approach so unique and avant-garde, being reviewed and
discussed:
• Sentic Computing's multi-disciplinary approach to
sentiment analysis-evidenced by the concomitant use of AI,
linguistics and psychology for knowledge representation and
inference
• Sentic Computing's multi-disciplinary approach to
sentiment analysis-evidenced by the concomitant use of AI,
linguistics and psychology for knowledge representation and
inference
• Sentic Computing’s shift from syntax to
semantics-enabled by the adoption of the bag-of-concepts model
instead of simply counting word co-occurrence frequencies in
text
• Sentic Computing’s shift from syntax to
semantics-enabled by the adoption of the bag-of-concepts model
instead of simply counting word co-occurrence frequencies in
text
• Sentic Computing's shift from statistics to
linguistics-implemented by allowing sentiments to flow from concept
to concept based on the dependency relation between clauses
• Sentic 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 Dr Amir Hussain and Dr Erik Cambria and will be of
interest to researchers in the fields of socially intelligent,
affective and multimodal human-machine interaction and systems.
This volume is the first in the Series Socio-Affective Computing
edited by Dr Amir Hussain and Dr Erik Cambria and will be of
interest to researchers in the fields of socially intelligent,
affective and multimodal human-machine interaction and systems.
