Transfer Learning Based on Utterance Emotion Corpus for Lyric Emotion Estimation
Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida, Kenji Kita, Fuji Ren · 2018
In prior research, there are various emotion estimation approaches that are corpus-based, dictionary-based, or rule-based. However, it is necessary to prepare language resources suitable to each domain, which will incur substantial cost. In this paper, we propose an approach to estimate emotion of lyric phrase by using transfer learning. We applied the existing utterance emotion corpus to the lyric emotion estimation task. As the result of an evaluation experiment, we obtained higher accuracy than the baseline method using a simple bag of words feature tf-idf weighted vector and logistic regression model.