SenticNet is an initiative conceived at the MIT Media Laboratory in 2009 within an industrial Cooperative Awards in Science and Engineering (CASE) research project born from the collaboration between the Media Lab, the University of Stirling, and Sitekit Solutions Ltd.
Since then, SenticNet has been further developed and applied for the design of emotion-aware intelligent applications in fields spanning from data mining to human-computer interaction.
The main aim of SenticNet is to make the conceptual and affective information conveyed by natural language (meant for human consumption) more easily-accessible to machines.
This is done by using the bag-of-concepts model, instead of simply counting word co-occurrence frequencies as in latent semantic indexing, and by leveraging on linguistic patterns, to allow sentiments to flow from concept to concept based on the dependency relation between clauses.
Currently, both the SenticNet knowledge base and the SenticNet framework are being maintained and further developed by the Sentic Team, a multidisciplinary research group based at the School of Computer Science and Engineering of Nanyang Technological University in Singapore, but also by many other sentic enthusiasts around the world.