An Improvement of Example-based Emotion Estimation Using Similarity between Sentence and each Corpus

Abstract

Example-based emotion estimators need an emotion corpus in which each sentences are assigned with emotion tags. It is difficult to determine emotion tags for the sentence consistently because of ambiguity of emotion. As a result, there are several wrong tags in a corpus. It causes decrease in the performance of an emotion estimation. In order to solve the problem, a new similarity between input sentence and emotion corpus is proposed. This similarity is based on frequencies of morpheme N-gram of the both input sentence and corpus. Experimental results show that the proposed method improves emotion precision from 60.3% to 81.8%.