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Automated Measures of Sentiment via Transformer- and Lexicon-Based Sentiment Analysis (TLSA)
  • Xinyan Zhao ,
  • Chau-Wai Wong
Xinyan Zhao
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Chau-Wai Wong
North Carolina State University, North Carolina State University

Corresponding Author:[email protected]

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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 communication scholars to adopt modern computational methods for valid and reliable sentiment analysis, we propose a free and open web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates different sentiment analysis tools including transformer-based deep learning models and sentiment dictionaries. TLSA enables users with limited computational resources to use these tools and validate their results at once. Two cases showed that transformer-based models provided more accurate measurement of sentiments in tweets, compared to lexicon-based methods. The performance of different tools varied to a large extent based on the dataset, suggesting the importance to validate different sentiment tools in a specific study. TLSA is expected to empower communication scholars to reap the benefit of state-of-the-art computational methods for valid measurement and insight discovery.
21 Nov 2023Published in Journal of Computational Social Science. 10.1007/s42001-023-00233-8