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arXiv · 2105.02947

On the logistical difficulties and findings of Jopara Sentiment Analysis

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

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Marvin M. Agüero-Torales, David Vilares, Antonio G. López-Herrera. 2021-05-06. On the logistical difficulties and findings of Jopara Sentiment Analysis. https://arxiv.org/abs/2105.02947

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