Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

Citation

Cambria E & Hussain A (2015) Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Socio-Affective Computing, 1. Cham, Switzerland: Springer International Publishing. http://www.springer.com/us/book/9783319236537

Abstract
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:• 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 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.

Keywords
Neurosciences; Data Mining and Knowledge Discovery; Semantics; Cognitive Psychology