Automated measures of sentiment via transformer- and lexicon-based sentiment analysis (TLSA)

Xinyan Zhao () and Chau-Wai Wong
Additional contact information
Xinyan Zhao: University of North Carolina at Chapel Hill
Chau-Wai Wong: North Carolina State University

Journal of Computational Social Science, 2024, vol. 7, issue 1, No 6, 145-170

Abstract: Abstract The last decade witnessed the proliferation of automated content analysis in communication research. However, existing computational tools have been taken up unevenly, with powerful deep learning algorithms such as transformers rarely applied as compared to lexicon-based dictionaries. To enable social scientists to adopt modern computational methods for valid and reliable sentiment analysis of English text, we propose an open and free web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates diverse tools and offers validation metrics, empowering users with limited computational knowledge and resources to reap the benefit of state-of-the-art computational methods. Two cases demonstrate the functionality and usability of TLSA. The performance of different tools varied to a large extent based on the dataset, supporting the importance of validating various sentiment tools in a specific context.

Keywords: Automated content analysis; Emotions; Sentiment analysis; Software engineering; Deep learning; Dictionaries (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-023-00233-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-023-00233-8

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-023-00233-8

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().