Exploiting grammatical dependencies for fine-grained opinion mining

Abstract

In any sentence, words are arranged in a proper sequence to communicate information. The complete meaning of a sentence is not only determined by the meaning of words, but also by the pattern in which words are arranged. Essentially each word in a sentence possesses grammatical corporations with other words for correct utterance of meaning, such corporations between words is called binary grammatical relation or dependency (BGD). We believe that the analysis of a sentence on the basis of grammatical dependencies among the words is very useful for application such as opinion mining (OM) from a domain specific free format product reviews. Such free format reviews are largely available on Web due to presence of many e-commerce sites. Because of unavailability of universal resources (e.g. opinion word lexicon and feature corpus) for all application domains, OM becomes a very challenging job. OM from free format product reviews mainly deals with extraction of domain specific features, identification of opinion words corresponding to each features and determination of semantic orientation (SO) of opinion of words (e.g. large, small, loud, etc), which does not belong to set prior polarity opinion words (e.g. beautiful, excellent, good, etc.). In this paper, we discuss the role of BGDs among the words of a sentence for opinion mining and explore many BGDs that can directly facilitate the OM.