TY - GEN
T1 - Label-Efficient Emotion and Sentiment Analysis
AU - Zhao, Sicheng
AU - Jia, Guoli
AU - Hong, Xiaopeng
AU - Zhao, Yanyan
AU - Tao, Jianhua
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/10/28
Y1 - 2024/10/28
N2 - Emotion and sentiment analysis (ESA) assists machines to serve humans more intelligently. However, collecting large-scale high-quality datasets for training ESA models in a supervised manner is expensive, time-consuming, and difficult in practice. This tutorial focuses on the label-efficient ESA (LeESA) learning methods. Specifically, we first introduce the stimuli and characteristics of emotion and then illustrate seven typical training paradigms, followed by applications and future directions of LeESA.
AB - Emotion and sentiment analysis (ESA) assists machines to serve humans more intelligently. However, collecting large-scale high-quality datasets for training ESA models in a supervised manner is expensive, time-consuming, and difficult in practice. This tutorial focuses on the label-efficient ESA (LeESA) learning methods. Specifically, we first introduce the stimuli and characteristics of emotion and then illustrate seven typical training paradigms, followed by applications and future directions of LeESA.
KW - affective computing
KW - emotion and sentiment analysis
KW - emotional intelligence
KW - label-efficient learning
UR - https://www.scopus.com/pages/publications/85209801985
U2 - 10.1145/3664647.3689173
DO - 10.1145/3664647.3689173
M3 - 会议稿件
AN - SCOPUS:85209801985
T3 - MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia
SP - 11300
EP - 11301
BT - MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
T2 - 32nd ACM International Conference on Multimedia, MM 2024
Y2 - 28 October 2024 through 1 November 2024
ER -