On the Logistical Difficulties and Findings of Jopara Sentiment Analysis
Title
On the Logistical Difficulties and Findings of Jopara Sentiment AnalysisDate
2021-06Citation
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.
Keywords
Code-switching language
Jopara
Guarani language
Spanish language
Sentiment analysis
Machine learning models
Jopara
Guarani language
Spanish language
Sentiment analysis
Machine learning models
Editor version
Rights
Atribución 4.0 Internacional