On the Logistical Difficulties and Findings of Jopara Sentiment Analysis

Agüero-Torales, Marvin M.; Vilares, David; López-Herrera, Antonio G.

Use this link to cite:

http://hdl.handle.net/2183/30534

On the Logistical Difficulties and Findings of Jopara Sentiment Analysis

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Aguero_Torales_Marvin_Vilares_David_Lopez_Herrera_Antonio_2021_Logistical_difficulties_Jopara_sentiment_Analysis.pdf (392.51 KB)

Identifiers

URI: http://hdl.handle.net/2183/30534

DOI: 10.18653/v1/2021.calcs-1.12

Publication date

2021-06

Authors

Agüero-Torales, Marvin M.

Vilares, David

López-Herrera, Antonio G.

Bibliographic citation

Marvin 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.

Abstract

[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.