Title: Aspect-level sentiment analysis for limited resources
Time: 2021year6monthAfternoon on the 18th2：30-4：30
Location:Computer Building313
Lecturer:Qian Tieyun seabet club，Wuhanseabet club
Abstract:With the rapid development of seabet club technology and the Internet，Users change from passive receivers of seabet club to active providers，There are a lot of incidents on the Internet、Product、Valuable review seabet club such as services，Express people’s emotional tendencies and opinions。The rapid expansion of network seabet club，Collection of seabet club、Higher requirements for processing and analysis，It also promotes the vigorous development of the research direction of sentiment analysis。
Aspect-level sentiment analysis is a fine-grained analysis task，Mining from text the emotional judgments people make about every aspect of an object, such as the screen and battery of a mobile phone。Compared to chapter or sentence level sentiment classification，Aspect-level sentiment analysis can provide more comprehensive and in-depth analysis。However，Lack of annotation data、Problems such as the low frequency of long-tail words have brought huge challenges to aspect-level sentiment analysis tasks，This report will introduce the recent work our team has done to address the above issues。
Qian Tieyun:seabet club of School of Computer Science, Wuhan University，Doctoral Supervisor，Main research direction is natural language processing、Web Mining。In top international seabet club conferences and journals (such as ACL、SIGIR、AAAI、EMNLP、CIKM、TKDE、TOIS、TKDD) published more than 70 papers。Served as TKDE、TWEB、TKDD、TOIS、Reviewer of "Journal of Computer Science" and other journals，and WWW、IJCAI、AAAI、ACL、 ICDM、Member of the program committee of CIKM and other famous international conferences.
