An Algorithm for Fuzzification of WordNets and its Application in Sentiment Analysis

WordNet-like Lexical Databases (WLDs) group English words into sets of synonyms called “synsets.” Synsets are utilized for several applications in the field of text mining. However, they were also open to criticism because although, in theory, not all the members (i.e. word senses) of a synset represent the meaning of that synset with the same degree, in practice, in WLDs they are considered as members of the synset identically. Correspondingly, the fuzzy version of synonym sets, called fuzzy-synsets were proposed. But, to the best or our knowledge. In this study, we present an algorithm for constructing fuzzy version of WLDs of any language, given a corpus of documents and a word-sense-disambiguation system of that language. A theoretical proof is also proposed for the validity of results of the proposed algorithm. Then, inputting the open-American-online-corpus (OANC) and UKB word-sense-disambiguation to the algorithm, we construct and publish online the fuzzified version English WordNet (FWN), and apply them in a Sentiment Analysis problem.