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2020 | OriginalPaper | Chapter

Opinion Transmission Network for Jointly Improving Aspect-Oriented Opinion Words Extraction and Sentiment Classification

Authors : Chengcan Ying, Zhen Wu, Xinyu Dai, Shujian Huang, Jiajun Chen

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.

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Metadata
Title
Opinion Transmission Network for Jointly Improving Aspect-Oriented Opinion Words Extraction and Sentiment Classification
Authors
Chengcan Ying
Zhen Wu
Xinyu Dai
Shujian Huang
Jiajun Chen
Copyright Year
2020
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-60449-3

Electronic ISBN: 978-3-030-60450-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-60450-9_50

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