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Discovering Polarity for Ambiguous and Objective Adjectives through Adverbial Modification

The field of opinion mining has emerged in recent years as an exciting challenge for computational linguistics: investigating how humans express subjective judgments through linguistic means paves the way for automatic recognition and summarization of opinionated texts, with the possibility of determining the polarities and strengths of opinions asserted. Sentiment lexicons are basic resources for investigating the orientation of a text that can be performed considering polarized words included in it but they encode the polarity of word types instead that the polarity of word tokens. The expression of an opinion through the choice of lexical items is context-sensitive and sentiment lexicons could be integrated with syntagmatic patterns that emerge as significant with statistical analyses. In this paper it will be proposed a corpus analysis of adverbially modified ambiguous (e.g. fast, rich) and objective adjectives (e.g. chemical, political) - that can be occasionally exploited to express a subjective judgments -. Comparing polarity encoded in sentiment lexicons and the results of a logistic regression analysis, the role of adverbial cues for polarity detection will be evaluated on the basis of a small sample of sentences manually annotated.