Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment AnalysisThis 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. |
Other editions - View all
Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment ... Erik Cambria,Amir Hussain No preview available - 2015 |
Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment ... Erik Cambria,Amir Hussain No preview available - 2019 |
Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment ... Erik Cambria,Amir Hussain No preview available - 2015 |
Common terms and phrases
according accuracy activation affective AffectiveSpace algorithm allows applications approach associated Cambria classifier cognitive collected combination common common-sense knowledge computing concepts considered contains database dataset defined dependency described detection developed dimension emotions evaluation example experience exploited expressions extracted facial fact final framework function hence human images important inference intelligence interaction knowledge base labels learning linguistic matrix means methods mind mood movie natural language negative neutral noun object obtained opinions particular patients performance polarity positive problem proposed reasoning relation represent representation retrieval rules Sect selection semantic sense sentence sentic computing sentic patterns SenticNet sentiment analysis shows similar social Source space specific structure Table tags tasks techniques troll understanding users vector verb visual