Aparício, SofiaAparício, João TiagoAparício, Manuela2024-02-202025-02-162024-02-15978-3-031-45650-3978-3-031-45651-02367-3370PURE: 83793207PURE UUID: 922ef2d4-084d-41af-8301-a5886a65b904Scopus: 85187665525WOS: 001259466400002ORCID: /0000-0003-4261-0344/work/153662148http://hdl.handle.net/10362/163852Aparício, S., Aparício, J. T., & Aparício, M. (2024). Multidimensional and Multilingual Emotional Analysis. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies: WorldCIST 2023, Volume 4 (Vol. 4, pp. 13-22). (Lecture Notes in Networks and Systems; Vol. 802). Springer. https://doi.org/10.1007/978-3-031-45651-0_2 --- 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.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.10735439engEmotional ratings of textAffective normsLong Short-Term MemoryRecurrent Neural NetworksMachine learningControl and Systems EngineeringSignal ProcessingComputer Networks and CommunicationsSDG 8 - Decent Work and Economic GrowthSDG 9 - Industry, Innovation, and Infrastructureconference object10.1007/978-3-031-45651-0_2https://github.com/keras-team/kerashttps://www.scopus.com/pages/publications/85187665525https://www.webofscience.com/wos/woscc/full-record/WOS:001259466400002