A complex nature of big data resources demands new methods for structuringespecially for textual content. WordNet is a good knowledge source forcomprehensive abstraction of natural language as its good implementations existfor many languages. Since WordNet embeds natural language in the form of acomplex network, a transformation mechanism WordNet2Vec is proposed in thepaper. It creates vectors for each word from WordNet. These vectors encapsulategeneral position - role of a given word towards all other words in the naturallanguage. Any list or set of such vectors contains knowledge about the contextof its component within the whole language. Such word representation can beeasily applied to many analytic tasks like classification or clustering. Theusefulness of the WordNet2Vec method was demonstrated in sentiment analysis,i.e. classification with transfer learning for the real Amazon opinion textualdataset.
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