Multimedia Tools and Applications

, Volume 59, Issue 2, pp 557-577

First online:

Sentic Computing for social media marketing

  • Erik CambriaAffiliated withDepartment of Computing Science and Mathematics, University of Stirling Email author 
  • , Marco GrassiAffiliated withDepartment of Biomedical, Electronic and Telecommunication Engineering, Universitá Politecnica delle Marche
  • , Amir HussainAffiliated withDepartment of Computing Science and Mathematics, University of Stirling
  • , Catherine HavasiAffiliated withMIT Media Laboratory, Massachusetts Institute of Technology

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In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.


AI Semantic Web Knowledge base management NLP Opinion mining and sentiment analysis