Abstract
In order to monitor informal political online discussions and to lead a better understanding of hate speech on social media, we found that it was necessary to use sentiment quantification for languages with few training datasets. Previous studies mainly rely on languages with enough data to train a model. Several statistical and machine learning models were produced and compared in three languages (English, Portuguese and Polish). This work shows promising results when inferring sentimental dimensions, even in languages other than English.
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Acknowledgement
We gratefully acknowledge financial support from FCT -Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant UIDB/04152/2020. This work is also supported by national funds through PhD grant (UI/BD/153587/2022) supported by FCT.
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Aparicio, S., Aparicio, J.T., Aparicio, M. (2024). Multidimensional and Multilingual Emotional Analysis. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 802. Springer, Cham. https://doi.org/10.1007/978-3-031-45651-0_2
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