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
来源：
SCIENTOMETRICS | 2025年 / 130卷 / 01期
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
相关论文
共 86 条
[41]  
Larrègue J, 2019, CAN J SOCIOL, V44, P95
[42]  
Lillis T, 2013, CRIT LANG LIT STUD, V17, P220
[43]  
Lindholm-Romantschuk Y., 1998, Scholarly Book Reviewing in the Social Sciences and Humanities: The Flow of Ideas within and among Disciplines
[44]   Linguistic positivity in soft and hard disciplines: temporal dynamics, disciplinary variation, and the relationship with research impact [J].
Liu, Xueying ;
Zhu, Haoran .
SCIENTOMETRICS, 2023, 128 (05) :3107-3127
[45]  
Loureiro D., 2022, ARXIV, DOI DOI 10.48550/ARXIV.2202.03829
[46]   Compliments and criticisms in book reviews about business communication [J].
Mackiewicz, Jo .
JOURNAL OF BUSINESS AND TECHNICAL COMMUNICATION, 2007, 21 (02) :188-215
[47]   Using Machine Learning to Investigate the Public's Emotional Responses to Work From Home During the COVID-19 Pandemic [J].
Min, Hanyi ;
Peng, Yisheng ;
Shoss, Mindy ;
Yang, Baojiang .
JOURNAL OF APPLIED PSYCHOLOGY, 2021, 106 (02) :214-229
[48]  
Mohammad S., 2010, P NAACL HLT 2010 WOR
[49]  
Moreno, 2008, J ENGL ACAD PURP, VVII, P15, DOI DOI 10.1016/J.JEAP.2008.02.009
[50]   Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains [J].
Mukhtar, Neelam ;
Khan, Mohammad Abid ;
Chiragh, Nadia .
TELEMATICS AND INFORMATICS, 2018, 35 (08) :2173-2183
123456789