@inproceedings{zhang-etal-2020-public,
title = "Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder",
author = "Zhang, Wenyue and
Li, Xiaoli and
Li, Yang and
Wang, Suge and
Li, Deyu and
Liao, Jian and
Zheng, Jianxing",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.307",
doi = "10.18653/v1/2020.emnlp-main.307",
pages = "3762--3767",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
%A Zhang, Wenyue
%A Li, Xiaoli
%A Li, Yang
%A Wang, Suge
%A Li, Deyu
%A Liao, Jian
%A Zheng, Jianxing
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-public
%X 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.
%R 10.18653/v1/2020.emnlp-main.307
%U https://aclanthology.org/2020.emnlp-main.307
%U https://doi.org/10.18653/v1/2020.emnlp-main.307
%P 3762-3767
Markdown (Informal)
[Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder](https://aclanthology.org/2020.emnlp-main.307) (Zhang et al., EMNLP 2020)
ACL