Sentiment Forecasting in Dialog
Zhongqing Wang, Xiujun Zhu, Yue Zhang, Shoushan Li, Guodong Zhou
Abstract
Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.
BibTeX
@inproceedings{wang-etal-2020-sentiment,
title = "Sentiment Forecasting in Dialog",
author = "Wang, Zhongqing and
Zhu, Xiujun and
Zhang, Yue and
Li, Shoushan and
Zhou, Guodong",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.221/",
doi = "10.18653/v1/2020.coling-main.221",
pages = "2448--2458"
}