Target-based Sentiment Annotation in Chinese Financial News

Chaofa Yuan, Yuhan Liu, Rongdi Yin, Jun Zhang, Qinling Zhu, Ruibin Mao, Ruifeng Xu


Abstract
This paper presents the design and construction of a large-scale target-based sentiment annotation corpus on Chinese financial news text. Different from the most existing paragraph/document-based annotation corpus, in this study, target-based fine-grained sentiment annotation is performed. The companies, brands and other financial entities are regarded as the targets. The clause reflecting the profitability, loss or other business status of financial entities is regarded as the sentiment expression for determining the polarity. Based on high quality annotation guideline and effective quality control strategy, a corpus with 8,314 target-level sentiment annotation is constructed on 6,336 paragraphs from Chinese financial news text. Based on this corpus, several state-of-the-art sentiment analysis models are evaluated.
Anthology ID:
2020.lrec-1.620
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5040–5045
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.620
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.620.pdf