SenticNet
SenticNet helps machines understand text by blending emotions, concepts, and cognition. It uses neurosymbolic AI and its Sentic Computing approach to treat text as concepts, combining symbolic and subsymbolic methods. Core technologies include Sentic Computing, neurosymbolic AI, deep learning, NLP, NLU, knowledge graphs, semantic networks, symbolic AI, and sub-symbolic AI. The project originates from MIT Media Lab and is applied to research and business use cases in social data analytics, human-computer interaction, finance, and healthcare. The SenticNet knowledge base and framework enable emotion-aware intelligent applications at scale.
SenticNet
SenticNet helps machines understand text by blending emotions, concepts, and cognition. It uses neurosymbolic AI and its Sentic Computing approach to treat text as concepts, combining symbolic and subsymbolic methods. Core technologies include Sentic Computing, neurosymbolic AI, deep learning, NLP, NLU, knowledge graphs, semantic networks, symbolic AI, and sub-symbolic AI. The project originates from MIT Media Lab and is applied to research and business use cases in social data analytics, human-computer interaction, finance, and healthcare. The SenticNet knowledge base and framework enable emotion-aware intelligent applications at scale.