Improving multi-label emotion classification via sentiment classification with dual attention transfer network

• Main Authors: YU, Jianfei, MARUJO, Luis, JIANG, Jing, KARUTURI, Pradeep, BRENDEL, William
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4279; https://ink.library.smu.edu.sg/context/sis_research/article/5282/viewcontent/Improving_Multi_label_Emotion_Class_2018_pv.pdf
• Institution: Singapore Management University

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.