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)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.semeval-1.123.pdf
- Data
- SentiMix