Abstract
In this paper, a special type of polysemantic words, that is, words with multiple meanings for the same part of speech, are analyzed under the name of neutrosophic words. These words represent the most difficult cases for the disambiguation algorithms as they represent the most ambiguous natural language utterances. For approximate their meanings, we developed a semantic representation framework made by means of concepts from neutrosophic theory and entropy measure in which we incorporate sense related data. We show the advantages of the proposed framework in a sentiment classification task.
Highlights
Every natural language word can have multiple realisations from the part-of-speech point of view, and for each of its possible parts-of-speech, it can have multiple meanings
The method we propose in this paper offers a knowledgebased solution for semantic word representation which targets sentiment classification and makes use of the general concepts of neutrosophic theory and entropy measure
Our proposal is described in conjunction with a sentiment analysis study in which the semantic word representation has the form of a three sentiment scores tuple
Summary
Every natural language word can have multiple realisations from the part-of-speech point of view, and for each of its possible parts-of-speech, it can have multiple meanings (especially the English words). These are quite new studies in the literature as the researchers in this area must be intrigued by the usability of sense level information in sentiment analysis Some researchers take this approach and compute the polarity score for each word sense [3], [4]. The method we propose in this paper offers a knowledgebased solution for semantic word representation which targets sentiment classification and makes use of the general concepts of neutrosophic theory and entropy measure. In this paper we concentrate our approach by keeping in mind only the most difficult cases for sentiment classification They are represented by a special class of polysemantic words with different meanings for the same part-of-speech realisation. The study proposed in this paper makes use of the SWN polarity scores of each word’s sense, this time in order to determine the overall sentiment score value.
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