Sentic computing is a multi-disciplinary approach to natural language processing and understanding 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. In sentic computing, whose term derives from the Latin ‘sentire’ (root of words such as sentiment and sentience) and ‘sensus’ (as in common-sense), the analysis of natural language is based on affective ontologies and common-sense reasoning tools, which enable the analysis of text not only at document-, page- or paragraph-level, but also at sentence-, clause-, and concept-level. In particular, sentic computing involves the use of AI and Semantic Web techniques, for knowledge representation and inference; mathematics, for carrying out tasks such as graph mining and multi-dimensionality reduction; linguistics, for discourse analysis and pragmatics; psychology, for cognitive and affective modeling; sociology, for understanding social network dynamics and social influence; finally ethics, for understanding related issues about the nature of mind and the creation of emotional machines. jumping NLP curves Sentic computing adopts the bag-of-concepts model in stead of simply counting word co-occurrence frequencies in text. Working at concept-level entails preserving the meaning carried by multi-word expressions such as ‘cloud computing’, which represent semantic atoms that should never be broken down into single words. In the bag-of-words model, for example, the concept ‘cloud computing’ would be split into ‘computing’ and ‘cloud’, which may wrongly activate concepts related to the weather and, hence, compromise categorization accuracy.
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