Dynamic Lexical Framework to Evaluate the Evolution of Emotions in Twitter
Pages 105 - 110
Abstract
Human emotions and sentiments are dynamic by nature. Nowadays, social networks have become a key resource for human communication and a faithful representation of this dynamism. This fact poses major challenges to those systems addressing sentiment analysis. Therefore, having systems capable of inferring this dynamism has become a key issue. In this paper we introduce Emoweb 2.0, a prototype for dynamic sentiment analysis of Twitter data. A well-known lexicon is taken as starting basis and new words are appended by an unsupervised learning algorithm governing the process. Sentiment values of new words are calculated and dynamically updated depending on the trends detected. Tweet sentiment scores are also computed during the process. A visualization module is included to observe word sentiment fluctuations over time. The experiment performed is based on the ongoing COVID-19 pandemic showing promising results.
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Index Terms
- Dynamic Lexical Framework to Evaluate the Evolution of Emotions in Twitter
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Published In
June 2021
123 pages
ISBN:9781450376846
DOI:10.1145/3477282
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Association for Computing Machinery
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Published: 17 December 2021
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- Spanish Ministry of Economy and Competitiveness
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ICSLT 2021
ICSLT 2021: 2021 7th International Conference on e-Society, e-Learning and e-Technologies
June 10 - 12, 2021
Portsmouth, United Kingdom
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