Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis

Rong Xiang, Yunfei Long, Qin Lu, Dan Xiong, I-Hsuan Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideographbased writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social
and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for
emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model
whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating
WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model.
Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics (ACL)
Pages91-96
Number of pages6
ISBN (Print)978-1-948087-80-3
DOIs
Publication statusPublished - 31 Oct 2018
Event9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Brussels, Belgium
Duration: 31 Oct 20181 Nov 2018

Conference

Conference9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Country/TerritoryBelgium
CityBrussels
Period31/10/181/11/18

Cite this