A Supplementary Feature Set for Sentiment Analysis in Japanese Dialogues

Abstract

Recently, real-time affect-awareness has been applied in several commercial systems, such as dialogue systems and computer games. Real-time recognition of affective states, however, requires the application of costly feature extraction methods and/or labor-intensive annotation of large datasets, especially in the case of Asian languages where large annotated datasets are seldom available. To improve recognition accuracy, we propose the use of cognitive context in the form of “emotion-sensitive” intentions. Intentions are often represented through dialogue acts and, as an emotion-sensitive model of dialogue acts, a tagset of interpersonal-relations-directing interpersonal acts (the IA model) is proposed. The model's adequacy is assessed using a sentiment classification task in comparison with two well-known dialogue act models, the SWBD-DAMSL and the DIT++. For the assessment, five Japanese in-game dialogues were annotated with labels of sentiments and the tags of all three dialogue act models which were used to enhance a baseline sentiment classifier system. The adequacy of the IA tagset is demonstrated by a 9% improvement to the baseline sentiment classifier's recognition accuracy, outperforming the other two models by more than 5%.