This paper presents an application of PageRank, a random-walk model originally devised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic property. The semantic properties we use for exemplifying the approach are positivity and negativity, two properties of central importance in sentiment analysis. The idea derives from the observation that WordNet may be seen as a graph in which synsets are connected through the binary relation "a term belonging to synset s k occurs in the gloss of synset s i ", and on the hypothesis that this relation may be viewed as a transmitter of such semantic properties. The data for this relation can be obtained from eXtended WordNet, a publicly available sensedisambiguated version of WordNet. We argue that this relation is structurally akin to the relation between hyperlinked Web pages, and thus lends itself to PageRank analysis. We report experimental results supporting our intuitions.
They then extend their work REF by applying the Page Rank algorithm to rank the WordNet senses in terms of how strongly a sense possesses a given semantic property (e.g., positive or negative).
PageRanking WordNet Synsets: An Application to Opinion Mining
45th Annual Meeting of the Association of Computational Linguistics
Computer Science