Agüero-Torales, Marvin M.Vilares, DavidLópez-Herrera, Antonio G.2022-04-262022-04-262021-06Marvin Agüero-Torales, David Vilares, and Antonio López-Herrera. 2021. On the logistical difficulties and findings of Jopara Sentiment Analysis. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 95–102, Online. Association for Computational Linguistics.http://hdl.handle.net/2183/30534[Abstract] This paper addresses the problem of sentiment analysis for Jopara, a code-switching language between Guarani and Spanish. We first collect a corpus of Guarani-dominant tweets and discuss on the difficulties of finding quality data for even relatively easy-to-annotate tasks, such as sentiment analysis. Then, we train a set of neural models, including pre-trained language models, and explore whether they perform better than traditional machine learning ones in this low-resource setup. Transformer architectures obtain the best results, despite not considering Guarani during pre-training, but traditional machine learning models perform close due to the low-resource nature of the problem.engAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Code-switching languageJoparaGuarani languageSpanish languageSentiment analysisMachine learning modelsconference outputopen access10.18653/v1/2021.calcs-1.12