Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/30120
Autoria: | Taborda, B. de Almeida, A. Dias, J. C. Batista, F. Ribeiro, R. |
Data: | 2023 |
Título próprio: | SA-MAIS: Hybrid automatic sentiment analyser for stock market |
Título da revista: | Journal of Information Science |
Volume: | N/A |
Referência bibliográfica: | Taborda, B., de Almeida, A., Dias, J. C., Batista, F., & Ribeiro, R. (2023). SA-MAIS: Hybrid automatic sentiment analyser for stock market. Journal of Information Science. https://dx.doi.org/10.1177/01655515231171361 |
ISSN: | 0165-5515 |
DOI (Digital Object Identifier): | 10.1177/01655515231171361 |
Palavras-chave: | Sentiment analysis Sentiment classification Sentiment lexicon Stock market Tweets |
Resumo: | Sentiment analysis of stock-related tweets is a challenging task, not only due to the specificity of the domain but also because of the short nature of the texts. This work proposes SA-MAIS, a two-step lightweight methodology, specially adapted to perform sentiment analysis in domain-constrained short-text messages. To tackle the issue of domain specificity, based on word frequency, the most relevant words are automatically extracted from the new domain and then manually tagged to update an existing domain-specific sentiment lexicon. The sentiment classification is then performed by combining the updated domain-specific lexicon with VADER sentiment analysis, a well-known and widely used sentiment analysis tool. The proposed method is compared with other well-known and widely used sentiment analysis tools, including transformer-based models, such as BERTweet, Twitter-roBERTa and FinBERT, on a domain-specific corpus of stock market-related tweets comprising 1 million messages. The experimental results show that the proposed approach largely surpasses the performance of the other sentiment analysis tools, reaching an overall accuracy of 72.0%. The achieved results highlight the advantage of using a hybrid method that combines domain-specific lexicons with existing generalist tools for the inference of textual sentiment in domain-specific short-text messages. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_95838.pdf | 652,91 kB | Adobe PDF | Ver/Abrir |
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