Subjectivity Word Sense Disambiguation

Many approaches to sentiment, and subjectivity analysis rely on lexicons of words that may be used to express subjectivity. Most subjectivity lexicons are compiled as lists of keywords, rather than word meanings (senses). However, many keywords have both subjective and objective senses. False hits -- subjectivity clues used with objective senses -- are a significant source of error in subjectivity and sentiment analysis. To tackle this source of error, we define a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. We hypothesize that SWSD is more feasible than full word sense disambiguation. We also hypothesize that SWSD can be exploited to improve the performance of contextual subjectivity analysis systems via sense-aware classification.