Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder

Wenyue Zhang, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, Jianxing Zheng


Abstract
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.
Anthology ID:
2020.emnlp-main.307
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3762–3767
Language:
URL:
https://aclanthology.org/2020.emnlp-main.307
DOI:
10.18653/v1/2020.emnlp-main.307
Bibkey:
Cite (ACL):
Wenyue Zhang, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, and Jianxing Zheng. 2020. Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3762–3767, Online. Association for Computational Linguistics.
Cite (Informal):
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (Zhang et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2020.emnlp-main.307.pdf
Video:
 https://slideslive.com/38938857