earticle

영어

This thesis, as part of the creation of a text-mining-based sentiment dictionary to be applied in the Korean grammar structure, solves the problem of the enhancement of accuracy of opinion mining data by applying the filtering model of candidate sentiment vocabularies. The fact that 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, which is attributable to the fact there isn’t a success factor in the filtering modeling standard for precise selection of vocabularies. In this thesis, a filtering model of positive and negative vocabularies on candidate Korean sentiment vocabularies and a reliability scale for accuracy were suggested to solve such problems by applying the semi-structured data filtering model for the selection of candidate sentiment vocabularies of the Korean grammar. The study has confirmed through relevant performance assessment when filtering were applied in relation to 30%, 50% and 60% respectively with regard to candidate sentiment vocabularies upon collecting vocabularies obtained via sentence segmentation and classification into positive and negative vocabularies that exceptional accuracy of the opinion sentiment dictionary was shown via the 60% filtering.