Semantic Simplification for Sentiment Classification
Xiaotong Jiang, Zhongqing Wang, Guodong Zhou
Abstract
Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture, since the sentiment is usually either expressed implicitly or shifted due to the occurrences of negation and rhetorical words. To this end, we enhance the original text with a sentiment-driven simplified clause to intensify its sentiment. The simplified clause shares the same opinion with the original text but expresses the opinion much more simply. Meanwhile, we employ Abstract Meaning Representation (AMR) for generating simplified clauses, since AMR explicitly provides core semantic knowledge, and potentially offers core concepts and explicit structures of original texts. Empirical studies show the effectiveness of our proposed model over several strong baselines. The results also indicate the importance of simplified clauses for sentiment classification.
BibTeX
@inproceedings{jiang-etal-2022-semantic,
title = "Semantic Simplification for Sentiment Classification",
author = "Jiang, Xiaotong and
Wang, Zhongqing and
Zhou, Guodong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.757/",
doi = "10.18653/v1/2022.emnlp-main.757",
pages = "11022--11032"
}