A Study for Sentiment Analysis Accepting the Diverse of Emotion Sensitivities

Abstract

In recent years, much research about sentiment analysis has focused on the emotional aspects of sentiment, such as the causes of emotions, and this study focuses on the "diverse of emotion sensitivities." In constructing datasets for sentiment analysis, it is common to set up various grammatical rules and word recognition criteria to guarantee labeling consistency because of the fluctuation in emotional understanding among annotators. However, strict criteria can cause biases, such as excluding the emotional expression that the reader naturally perceives from the annotation targets partially. Therefore, in this study, we propose a policy for the intuitive annotation of emotional expressions by readers. Then, we analyze the fluctuation of emotional interpretation and annotations expressed with the constructed dataset. In addition, we evaluate the ability of semi-supervised learning using unlabeled data to absorb the fluctuation of polarity expressions and labels.