Affectivespace 2: Enabling Affective Intuition For Concept-Level Sentiment Analysis
PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE(2015)
摘要
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 concepts. By reducing the dimensionality of affective common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving efficient concept-level sentiment analysis.
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