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Taborda, B., de Almeida, A., Dias, J. C., Batista, F. & Ribeiro, R. (N/A). SA-MAIS: Hybrid automatic sentiment analyser for stock market. Journal of Information Science. N/A

B. M. Taborda et al.,  "SA-MAIS: Hybrid automatic sentiment analyser for stock market", in Journal of Information Science, vol. N/A, N/A

@article{tabordaN/A_1732201389163,
	author = "Taborda, B. and de Almeida, A. and Dias, J. C. and Batista, F. and Ribeiro, R.",
	title = "SA-MAIS: Hybrid automatic sentiment analyser for stock market",
	journal = "Journal of Information Science",
	year = "N/A",
	volume = "N/A",
	number = "",
	doi = "10.1177/01655515231171361",
	url = "https://doi.org/10.1177/01655515231171361"
}

TY  - JOUR
TI  - SA-MAIS: Hybrid automatic sentiment analyser for stock market
T2  - Journal of Information Science
VL  - N/A
AU  - Taborda, B.
AU  - de Almeida, A.
AU  - Dias, J. C.
AU  - Batista, F.
AU  - Ribeiro, R.
PY  - N/A
SN  - 0165-5515
DO  - 10.1177/01655515231171361
UR  - https://doi.org/10.1177/01655515231171361
AB  - 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.
ER  -