Wenyue Zhang


pdf bib
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Wenyue Zhang | Xiaoli Li | Yang Li | Suge Wang | Deyu Li | Jian Liao | Jianxing Zheng
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Detecting public sentiment drift is a challenging task due to sentiment change over time. Existing methods first build a classification model using historical data and subsequently detect drift if the model performs much worse on new data. In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data.Our experimental results demonstrate that our proposed model achieves better results than three existing state-of-the-art methods.