Label-Efficient Emotion and Sentiment Analysis

摘要

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.