SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis

Citation

Cambria E, Havasi C & Hussain A (2012) SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: Youngblood G & McCarthy P (eds.) Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-12. Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, FLAIRS 12, Marco Island, FL, USA, 23.05.2012-25.05.2012. Palo Alto, CA, USA: Association for the Advancement of Artificial, pp. 202-207. http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/viewPDFInterstitial/4411/4794

Abstract
Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and SemanticWeb techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.