RANKING RADICALLY IMPLICIT WEB FORUM USERS BY ASPECT SCORE

Computer Science and Engineering

In the computational linguistics the extraction of actual sense of words from text has a long history in the field. Due to its importance in the field of sentiment analysis it is considered the most important one. During sentiment analysis more challenging problems are faced due to the ambiguous senses of words. In this work we propose a new method of word sense Disambiguation (WSD) using matrix map of the semantic scores extracted from SentiWordNet of WordNet glosses terms. The correct sense of the target word is extracted and determined for which the similarity between WordNet gloss and context matrix is greatest. Our empirical results have shown that the proposed method improves the result of sentence level sentiment classification as evaluated on different domain datasets. From the result it is clear that the propose method achieves an accuracy of 90.71% at sentence level sentiment classification of online reviews. Sentiment Classification (SC) is about assigning a positive, negative or neutral label to piece of text based on its overall opinion. This paper describes our in-progress work on extracting the meaning of words for SC. In particular, we investigate the utility of sense-level polarity information for SC. We first show that methods based on common classification features are not robust and their performance varies widely across different domains. We then show that sense-level polarity information features can significantly improve the performance of SC. We use datasets in different domains to study the robustness of the designated features . Our preliminary results show that the most common sense of the words result in the most robust results across different domains. In addition our observation shows that the sense level polarity information is useful for producing a set of high-quality seed words which can be used for further improvement of SC task.

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