On the logistical difficulties and findings of Jopara Sentiment Analysis

Marvin Agüero-Torales, David Vilares, Antonio López-Herrera


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.
Anthology ID:
2021.calcs-1.12
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–102
Language:
URL:
https://aclanthology.org/2021.calcs-1.12
DOI:
10.18653/v1/2021.calcs-1.12
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.12.pdf
Code
 mmaguero/josa-corpus