@inproceedings{ohman-2021-validity,
title = "The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research",
author = {{\"O}hman, Emily},
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
Alnajjar, Khalid and
Partanen, Niko and
Rueter, Jack},
booktitle = "Proceedings of the Workshop on Natural Language Processing for Digital Humanities",
month = dec,
year = "2021",
address = "NIT Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.nlp4dh-1.2",
pages = "7--12",
abstract = "Lexicon-based sentiment and emotion analysis methods are widely used particularly in applied Natural Language Processing (NLP) projects in fields such as computational social science and digital humanities. These lexicon-based methods have often been criticized for their lack of validation and accuracy {--} sometimes fairly. However, in this paper, we argue that lexicon-based methods work well particularly when moving up in granularity and show how useful lexicon-based methods can be for projects where neither qualitative analysis nor a machine learning-based approach is possible. Indeed, we argue that the measure of a lexicon{'}s accuracy should be grounded in its usefulness.",
}
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%0 Conference Proceedings
%T The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research
%A Öhman, Emily
%Y Hämäläinen, Mika
%Y Alnajjar, Khalid
%Y Partanen, Niko
%Y Rueter, Jack
%S Proceedings of the Workshop on Natural Language Processing for Digital Humanities
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar, India
%F ohman-2021-validity
%X Lexicon-based sentiment and emotion analysis methods are widely used particularly in applied Natural Language Processing (NLP) projects in fields such as computational social science and digital humanities. These lexicon-based methods have often been criticized for their lack of validation and accuracy – sometimes fairly. However, in this paper, we argue that lexicon-based methods work well particularly when moving up in granularity and show how useful lexicon-based methods can be for projects where neither qualitative analysis nor a machine learning-based approach is possible. Indeed, we argue that the measure of a lexicon’s accuracy should be grounded in its usefulness.
%U https://aclanthology.org/2021.nlp4dh-1.2
%P 7-12
Markdown (Informal)
[The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research](https://aclanthology.org/2021.nlp4dh-1.2) (Öhman, NLP4DH 2021)
ACL