@inproceedings{c1d6cdeab585454e9dd5e1bc73f21074,
title = "Assessing the Effectiveness of Affective Lexicons for Depression Classification",
abstract = "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.",
keywords = "depression analysis, affective lexicon, language analysis",
author = "{Abd Yusof}, {Noor Fazilla} and Chenghua Lin and Frank Guerin",
year = "2018",
month = may,
day = "22",
language = "English",
isbn = "978-3-319-91946-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer ",
pages = "65--69",
editor = "M Silberztein and {Atigui }, F and {Kornyshova }, E and {M{\'e}tais }, E and {Meziane }, F",
booktitle = "Natural Language Processing and Information Systems",
note = "23rd International Conference on Natural Language & Information Systems, NLDB ; Conference date: 13-06-2018 Through 15-06-2018",
}