Classification Technique for Filtering Sentiment Vocabularies for the Enhancement of Accuracy of Opinion Mining

Abstract

This thesis solves the problem of the enhancement of the accuracy of opinion mining data by applying the filtering model of candidate sentiment vocabularies as a way of constructing a text mining-based sentiment dictionary to be applied in the Korean grammar structure. The fact the reliability of sensitive vocabularies shows huge variances according to the filtering modeling method applied has become a decreasing factor for the accuracy of the vocabularies in the opinion mining process, and this is primarily due to the fact there isn't a success factor in the filtering modeling standard for precise selection of vocabularies. In this thesis, a text filtering modeling for the selection of candidate sentiment vocabularies of the processed Korean grammar were suggested to solve such problems. Moreover, a filtering model of positive and negative vocabularies on candidate Korean sentiment vocabularies and a reliability scale for accuracy were suggested in this thesis by applying the semi- structured data filtering model.