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.- Anthology ID:
- 2021.nlp4dh-1.2
- Volume:
- Proceedings of the Workshop on Natural Language Processing for Digital Humanities
- Month:
- December
- Year:
- 2021
- Address:
- NIT Silchar, India
- Venue:
- NLP4DH
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 7–12
- Language:
- URL:
- https://aclanthology.org/2021.nlp4dh-1.2
- DOI:
- Cite (ACL):
- Emily Öhman. 2021. The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research. In Proceedings of the Workshop on Natural Language Processing for Digital Humanities, pages 7–12, NIT Silchar, India. NLP Association of India (NLPAI).
- Cite (Informal):
- The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research (Öhman, NLP4DH 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.nlp4dh-1.2.pdf