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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.
A Dynamic Gesture Recognition System based on Trajectory Data of the Motion-sphere
Jaeyeong Ryu, Adithya B, Ashok Kumar Patil, Youngho Chai
http://doi.org/10.5626/JOK.2021.48.7.781
Recently, dynamic gesture recognition technology, which belongs to human-computer interaction (HCI), has received much attention. This is because the interface configuration for utilizing the system is simple and it is possible to communicate quickly. In this paper, we used a new input data format for the dynamic gesture recognition system and conducted research to improve the recognition accuracy. In the existing dynamic gesture recognition system, the position data and the rotation data of the joint are mainly used. In the proposed system, motion-sphere trajectory data are used. Motion-sphere expresses motion intuitively as a technique for visualizing movement. In the motion-sphere, the expression is composed of the trajectory and twist angle. In this paper, the trajectory of the motion-sphere is used as input data of the dynamic gesture recognition system. The validity of the trajectory data used is verified through the dynamic gesture recognition accuracy comparison. In the experiment, we experimented on two cases. The first cases were conducted by using measured quaternion data. The other experiments used open motion data. Both experiments conducted cognitive accuracy tests, and each experiment yielded high cognitive accuracy.
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