AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis


  • Erik Cambria Nanyang Technological University
  • Jie Fu National University of Singapore
  • Federica Bisio University of Genoa
  • Soujanya Poria Nanyang Technological University



Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language con- cepts. By reducing the dimensionality of affec- tive common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intu- itively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit fea- tures, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving efficient concept-level sentiment analysis.




How to Cite

Cambria, E., Fu, J., Bisio, F., & Poria, S. (2015). AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



AAAI Technical Track: Cognitive Systems