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A corpus-based study on Chinese sentiment parameters of Chinese sentiment discourse

Bibliographic Data

ID23024437
AuthorsXiao-Ling Cui (Beijing University of Posts and Telecommunications, Beijing), Cui Xiao-ling (Beijing University of Posts and Telecommunications), Janet S Shibamoto-Smith (University of California, Davis), Janet S Shibamoto‐smith (University of California, Davis)
Year2014
Volume5
Issue2
Pages185-210
Publication date2014-12-05
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueChinese Language and Discourse An International and Interdisciplinary Journal (JOURNAL)
Journal identifiersISSN: 1877-7031 • E-ISSN: 1877-8798
PublisherJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/cld.5.2.03cui
OpenAlexW2050045842
LanguageEN
References cited9

Most previous work on sentiment identification and annotation has focused on the identification and annotation of attitudes and targets, while less work has been done on other sentiment parameters. In this paper, we aim to discover different lexical, syntactic and semantic features of Chinese sentiment parameters based on Appraisal Theory. The data are from an annotated corpus of Chinese commentaries, analyzed using both qualitative and quantitative methods. We find that sentence-level sentiment production is the collaborative work of the core sentiment parameter (attitude) with other peripheral sentiment parameters (topic, source, field, process and degree of attitude). The distribution of sentiment parameters is also restricted by word classes, syntactic and semantic features and functions. This work not only offers a new analytic framework for Chinese sentiment analysis, but will improve the precision of sentence-level machine extraction of sentiment expressions in Chinese, with implications for possible extension to other languages.

Annotation · Automatic summarization · Field (mathematics) · Identification (biology) · Natural language processing · Part of speech · Process (computing) · Sentence · Sentiment analysis · Advanced Text Analysis Techniques · Artificial Intelligence · Computer Science · Sentiment Analysis and Opinion Mining · Topic Modeling

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