@inproceedings{angel-etal-2020-nlp,
title = "{NLP}-{CIC} at {S}em{E}val-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier",
author = "Angel, Jason and
Aroyehun, Segun Taofeek and
Tamayo, Antonio and
Gelbukh, Alexander",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.123/",
doi = "10.18653/v1/2020.semeval-1.123",
pages = "957--962",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier
%A Angel, Jason
%A Aroyehun, Segun Taofeek
%A Tamayo, Antonio
%A Gelbukh, Alexander
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F angel-etal-2020-nlp
%X 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.
%R 10.18653/v1/2020.semeval-1.123
%U https://aclanthology.org/2020.semeval-1.123/
%U https://doi.org/10.18653/v1/2020.semeval-1.123
%P 957-962
Markdown (Informal)
[NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier](https://aclanthology.org/2020.semeval-1.123/) (Angel et al., SemEval 2020)
ACL