Journal
BEHAVIOR RESEARCH METHODS
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.3758/s13428-023-02068-7
Keywords
Chinese characters; Valence; Sentiment compositionality; Affect representation
Categories
Ask authors/readers for more resources
Sentiment analysis in Chinese natural language processing is usually based on sentiment annotations of words. This study collected sentiment annotations for 3827 Chinese characters, which demonstrated high reliability and were validated through comparison with word equivalents. The study also investigated the relationship between characters and other lexico-semantic variables, as well as character processing efficiency. The analysis of the association between character valence and word valence revealed semantic compositionality and sentiment fusion in larger Chinese linguistic units. These character ratings expand current Chinese sentiment lexicons and can be used for more accurate stimuli assessment and efficient sentiment analysis.
Sentiment analysis in Chinese natural language processing has been largely based on words annotated with sentiment categories or scores. Characters, however, are the basic orthographic, phonological, and in most cases, semantic units in the Chinese language. This study collected sentiment annotations for 3827 characters. The ratings demonstrated high levels of reliability, and were validated through a comparison with the ratings of some characters' word equivalents reported in a previous norming study. Relations with other lexico-semantic variables and character processing efficiency were investigated. Furthermore, analyses of the association between constituent character valence and word valence revealed semantic compositionality and sentiment fusion characteristic of larger Chinese linguistic units. These ratings for characters, expanding current Chinese sentiment lexicons, can be utilized for the purposes of more precise stimuli assessment in research on Chinese character processing and more efficient sentiment analysis equipped with annotations of single-character words.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available