NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier

Jason Angel, Segun Taofeek Aroyehun, Antonio Tamayo, Alexander Gelbukh


Abstract
Code-switching is a phenomenon in which two or more languages are used in the same message. Nowadays, it is quite common to find messages with languages mixed in social media. This phenomenon presents a challenge for sentiment analysis. In this paper, we use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages. Our simple approach achieved a F1-score of 0:71 on test set on the competition. We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.
Anthology ID:
2020.semeval-1.123
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
957–962
Language:
URL:
https://aclanthology.org/2020.semeval-1.123
DOI:
10.18653/v1/2020.semeval-1.123
Bibkey:
Cite (ACL):
Jason Angel, Segun Taofeek Aroyehun, Antonio Tamayo, and Alexander Gelbukh. 2020. NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 957–962, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier (Angel et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.123.pdf
Data
SentiMix