We present a classifier to predict contextual polarity of subjective phrases in a sentence. Our approach features lexical scoring derived from the Dictionary of Affect in Language (DAL) and extended through WordNet, allowing us to automatically score the vast majority of words in our input avoiding the need for manual labeling. We augment lexical scoring with n-gram analysis to capture the effect of context. We combine DAL scores with syntactic constituents and then extract ngrams of constituents from all sentences. We also use the polarity of all syntactic constituents within the sentence as features. Our results show significant improvement over a majority class baseline as well as a more difficult baseline consisting of lexical n-grams.
Again in phrase level, Agrawal et al. REF proposed a method to predict contextual polarity of subjective phrases in a sentence.
Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams
EACL
Computer Science