The diachronic change in linguistic positivity in the academic book reviewing of language studies: a text-mining analysis

被引:0
作者
Liu, Xueying [1 ]
Zhu, Haoran [1 ]
机构
[1] Huazhong Univ Sci & Technol, 1037 Luoyu Rd, Wuhan, Peoples R China
关键词
Linguistic positivity; Book reviews; Text mining; Sentiment analysis; MACHINE-LEARNING APPROACH; SENTIMENT ANALYSIS; ENGLISH; COMPLIMENTS; POLITENESS; WORDS;
D O I
10.1007/s11192-024-05208-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Previous studies found an increasing trend of linguistic positivity in academic texts. That is, there is a tendency towards using more positive language than negative language in academic writing. However, most of these studies included research articles as the corpus data. Little is known about whether such a trend exists in book reviews, an important academic genre that features evaluative language. Thus, this paper presents a pilot study on the features and temporal dynamics of the positive/negative language of book reviews in the discipline of linguistics. Based on a corpus of 1550 book reviews published between 1990 and 2020, it was found that book reviews have experienced a significant upward trend in terms of both positive words and sentiment scores. Possible reasons and implications of the findings are discussed.
引用
收藏
页码:133 / 157
页数:25
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