運用遷移式學習改善BERT於中文歌詞情緒分類模型之研發

A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics

音樂庫的爆炸增長讓音樂資訊檢索和推薦成為重要議題，以音樂情緒辨識為基礎的推薦系統逐漸受到研究者的重視。音樂情緒辨識主要以歌曲情緒為主，部分研究關注英文歌詞，罕見對於中文歌詞情緒辨識的研究。因此，本研究提出利用BERT預訓練模型和遷移學習來改善中文歌詞的情緒分類任務。實驗結果顯示，在未針對歌詞情緒分類任務訓練下：(a)使用BERT針對CVAT建立之分類模型，只能達到50%的歌詞情緒分類準確度。(b)使用BERT針對CVAW+CVAP建立分類模型再對CVAT資料集遷移學習後，能提升到71%的歌詞情緒分類準確度。

The explosive growth of music libraries has made music information retrieval and recommendation a critical issue. Recommendation systems based on music emotion recognition are gradually gaining attention. Most of the studies focus on audio data rather than lyrics to build models of music emotion classification. In addition, because of the richness of English language resources, most of the existing studies are focused on English lyrics but rarely on Chinese. For this reason, We propose an approach that uses the BERT pretraining model and Transfer learning to improve the emotion classification task of Chinese lyrics. The following approaches were used without any specific training for the Chinese lyrics emotional classification task: (a) Using BERT, only can reach 50% of the classification accuracy. (b) Using BERT with transfer learning of CVAW, CVAP, and CVAT datasets can achieve 71% classification accuracy.