KcBert-based Movie Review Corpus Emotion Analysis Using Emotion Vocabulary Dictionary

Yeonji Jang, Jiseon Choi, Hansaem Kim

http://doi.org/10.5626/JOK.2022.49.8.608

Emotion analysis is the classification of human emotions expressed in text data into various emotional types such as joy, sadness, anger, surprise, and fear. In this study, using the emotion vocabulary dictionary, the emotions expressed in the movie review corpus were classified into nine categories: joy, sadness, fear, anger, disgust, surprise, interest, boredom, and pain to construct an emotion corpus. Then, the performance of the model was evaluated by training the emotion corpus in KcBert. To build the emotion analysis corpus, an emotion vocabulary dictionary based on a psychological model was used. It was judged whether the vocabulary of the emotion vocabulary dictionary and the emotion vocabulary displayed in the movie review corpus matched, and the emotion type matching the vocabulary appearing at the end of the movie review corpus was tagged. Based on the performance of the emotion analysis corpus constructed in this way by training it on KcBert pre-trained with NSMC, KcBert showed excellent performance in the model classified into 9 types.