%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