We introduce ConnotationWordNet, a connotation lexicon over the network of words in conjunction with senses. We formulate the lexicon induction problem as collective inference over pairwise-Markov Random Fields, and present a loopy belief propagation algorithm for inference. The key aspect of our method is that it is the first unified approach that assigns the polarity of both word-and sense-level connotations, exploiting the innate bipartite graph structure encoded in WordNet. We present comprehensive evaluation to demonstrate the quality and utility of the resulting lexicon in comparison to existing connotation and sentiment lexicons.
REF present a unified model that assigns connotation polarities to both words and senses.
ConnotationWordNet: Learning Connotation over the Word+Sense Network
ACL
Computer Science