2023
DOI: 10.7717/peerj-cs.1377
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Evaluation of transformer models for financial targeted sentiment analysis in Spanish

Abstract: Nowadays, financial data from social media plays an important role to predict the stock market. However, the exponential growth of financial information and the different polarities of sentiment that other sectors or stakeholders may have on the same information has led to the need for new technologies that automatically collect and classify large volumes of information quickly and easily for each stakeholder. In this scenario, we conduct a targeted sentiment analysis that can automatically extract the main ec… Show more

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Cited by 9 publications
(3 citation statements)
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“…In Spanish, the work described in [2] describes an MTSA strategy composed of several individual models that are able to detect the MET and obtain its polarity and the sentiments toward other companies and citizens. This work also proposes a novel corpus composed of two data sources: tweets about finance and headlines from digital newspapers, both in Spanish.…”
Section: Multitarget Sentiment Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…In Spanish, the work described in [2] describes an MTSA strategy composed of several individual models that are able to detect the MET and obtain its polarity and the sentiments toward other companies and citizens. This work also proposes a novel corpus composed of two data sources: tweets about finance and headlines from digital newspapers, both in Spanish.…”
Section: Multitarget Sentiment Analysismentioning
confidence: 99%
“…However, this approach does not take into account the MET extraction. It is worth mentioning that the [2] dataset has been extended and used as a common task in IberLEF 2023 [3].…”
Section: Multitarget Sentiment Analysismentioning
confidence: 99%
“…In recent years, transformer-based models have revolutionized the field of NLP by achieving the best results in many tasks like text classification [14], [15], question answering, named entity recognition [16], [17], and sentiment analysis [18], [19]. In addition, these models have obtained promising results in hate speech detection in different languages, including Arabic [20]- [23].…”
Section: Introductionmentioning
confidence: 99%