Aspect-level Sentiment Analysis: Techniques and Datasets

Abstract

Aspect-level sentiment analysis is a crucial problem in fine-grained sentiment analysis, which aims to automatically predict the sentiment polarity of the specific aspect in its context. In this talk, Dr. Yang will introduce several aspect-level sentiment analysis works: (i) feature enhanced attention network, which fully learns the connections among content words, aspect words and emotion words in sentences from different sub-spaces through multi-view mutual attention mechanism; (ii) hierarchical human-like strategy, which mimics the process of how humans identify the sentiment of a piece of text given a specific aspect; (iii) a large-scale multi-aspect multi-sentiment dataset, which would push forward the research of aspect-level sentiment analysis.

Biography

Min Yang is currently an assistant professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. She received her Ph.D. degree from the department of computer science, the University of Hong Kong in 2017. Her current research interests include natural language processing, data mining, recommendation systems. Dr. Yang has more than 70 international, peer-reviewed publications on top-tier conferences or journals, such as ACL, SIGIR, WWW, KDD, AAAI, IJCAI, TKDE, TMM, etc.