Dimensional sentiment analysis has received considerable attentions because it can represent affective states as continuous numerical values on multiple dimensions such as valence (positive‒negative) and arousal (excited‒calm). Compared to the categorical approach that represent affective states as several discrete classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. This talk introduces machine learning methods for dimensional sentiment analysis and its applications on health policy, finance, education and social media.
Liang-Chih Yu is a professor in the Department of Information Management at Yuan Ze University in Taiwan, R.O.C. He received his Ph.D. in Computer Science and Information Engineering from National Cheng Kung University in Taiwan, R.O.C. He was a visiting scholar at the Natural Language Group, Information Sciences Institute, University of Southern California (USC/ISI) from 2007 to 2008, and at DOCOMO Innovations for three months in 2018. He is currentlyBoard Member and Convener of SIGCALL of the Association for Computational Linguistics and Chinese Language Processing (ACLCLP), and serves as an editorial board member of International Journal of Computational Linguistics and Chinese Language Processing. His research interests include natural language processing, sentiment analysis, computer-assisted language learning. His team has developed systems that ranked first in IJCNLP 2017 Task 4: Customer Feedback Analysis, and second in the recent SemEval and BEA shared task competitions. His research has appeared in leading journals including Artificial Intelligence, ACM/IEEE Transactions, Decision Support Systems, and Knowledge-based Systems, and has presented at leading conferences including ACL, EMNLP, COLING, NAACL, and elsewhere.