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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2020.emnlp-main.307.pdf