SenticNet is a publicly available semantic resource for concept-level sentiment analysis. The affective common-sense knowledge base is built by means of sentic computing, a paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions over the Web.
In particular, SenticNet exploits an ensemble of graph-mining and dimensionality-reduction techniques to bridge the conceptual and affective gap between word-level natural language data and the concept-level opinions and sentiments conveyed by them.
SenticNet is a knowledge base that can be employed for the development of applications in fields such as big social data analysis, human-computer interaction, and e-health. More details on how the different versions of SenticNet were built and evaluated can be found in three papers published in AAAI-CSK-10, AAAI-FLAIRS-12, and AAAI-14, respectively.