Continuous models of affect from text using n-grams

We propose a method of affective text analysis and modeling that is capable of generating continuous valence ratings at the sentence level starting from word and multi-word term valence ratings. Motivated from the language modeling literature, a back-off algorithm is employed to efficiently fuse the valence of single-word and multi-word terms. Specifically, a term detection criterion is used to select the appropriate n-gram terms, starting with bigrams and potentially backing off to unigrams. Term affective ratings are generated by a lexicon expansion method, using semantic similarity estimates computed on a large web corpus. The proposed framework provides state-of-the art results in the sentence level SemEval'07 task of news headline polarity detection, reaching an accuracy of 75%.