@article{Cambria_Fu_Bisio_Poria_2015, title={AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9230}, DOI={10.1609/aaai.v29i1.9230}, abstractNote={ <p> 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. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cambria, Erik and Fu, Jie and Bisio, Federica and Poria, Soujanya}, year={2015}, month={Feb.} }