Adapting computational text analysis to social science (and vice versa)

被引:122
作者
DiMaggio, Paul [1 ]
机构
[1] Princeton Univ, Dept Sociol, 130 Mercer St, Princeton, NJ 08540 USA
关键词
Topic models; text analysis; unsupervised models; interpretation; sentiment analysis; supervised models;
D O I
10.1177/2053951715602908
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Social scientists and computer scientist are divided by small differences in perspective and not by any significant disciplinary divide. In the field of text analysis, several such differences are noted: social scientists often use unsupervised models to explore corpora, whereas many computer scientists employ supervised models to train data; social scientists hold to more conventional causal notions than do most computer scientists, and often favor intense exploitation of existing algorithms, whereas computer scientists focus more on developing new models; and computer scientists tend to trust human judgment more than social scientists do. These differences have implications that potentially can improve the practice of social science.
引用
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页数:5
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