Assessing the Effectiveness of Affective Lexicons for Depression Classification

被引：3
|
作者：
Abd Yusof, Noor Fazilla [1 ]
Lin, Chenghua [1 ]
Guerin, Frank [1 ]
机构：
[1] Univ Aberdeen, Comp Sci, Aberdeen, Scotland
来源：
NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS (NLDB 2018) | 2018年 / 10859卷
基金：
英国工程与自然科学研究理事会;
关键词：
Depression analysis; Affective lexicon; Language analysis;
D O I：
10.1007/978-3-319-91947-8_7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Affective lexicons have been commonly used as lexical features for depression classification, but their effectiveness is relatively unexplored in the literature. In this paper, we investigate the effectiveness of three popular affective lexicons in the task of depression classification. We also develop two lexical feature engineering strategies for incorporating those lexicons into a supervised classifier. The effectiveness of different lexicons and feature engineering strategies are evaluated on a depression dataset collected from LiveJournal.
引用
收藏
页码：65 / 69
页数：5
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