ConnotationWordNet: Learning Connotation over the Word+Sense Network

We introduce ConnotationWordNet, a con-notation lexicon over the network of words in conjunction with senses. We formulate the lexicon induction problem as collec-tive inference over pairwise-Markov Ran-dom 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 bipar-tite 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.