The transformation of the Web from its humble read-only origins to the current interactive/read-write medium has made its users ever more keen to share their sentiments and opinions through blogs, wikis, fora, chats and social networks, giving birth to a sort of social affective intelligence that emerges from the integration, competition and cooperation of web-users.
The distillation of knowledge from such type of social affective data is a key factor for applications in fields such as commerce, tourism, education and health, but the extremely unstructured nature of these contents and their exponentially growing size make it a very difficult task.
Such a challenge requires the ability to both represent and perform reasoning on social affective data, tasks which have been separately undertaken by Semantic Web and AI researchers respectively, exploiting techniques such as RDF schemas, triple stores, ontology languages, machine learning, neural networks and computational intelligence.
The aim of this special session is to bring these two complementary groups together across the disciplinary divide for possibly the very first time, exploring the new frontiers of opinion mining and sentiment analysis.

Topics of Interest:
- Social affective data mining, search, query and ranking
- Social affective data analysis, tagging, indexing and classification
- Social affective data creation, editing, authoring and sharing
- Social affective data processing techniques and applications

Schedule:
- Submission Deadline: December 1st, 2010
- Notification of Acceptance: January 1st, 2011
- Final Manuscripts Due: February 1st, 2011

Organizers:
- Amir Hussain, University of Stirling, UK
- Tariq S. Durrani, University of Strathclyde, UK
- Erik Cambria, University of Stirling, UK