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€� Â{u396E}tic Computing's multi-disciplinary approach to sentimentÂ{u2821}analysis-evidenced byÂ{u4A25} concomitant use of AI, linguistics and psychology for knowledge representation and inference â€� Â{u396E}tic Computingâ€{u3833}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€� Â{u396E}tic 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 byÂ{u4CA0} Amir HussainÂ{u086E}d Dr Erik CambriaÂ{u1BA4} will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.