@inproceedings{a77bf2db100448fbbdb5a47a2003e8c1,
title = "NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier",
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.",
author = "Jason Angel and Aroyehun, {Segun Taofeek} and Antonio Tamayo and Alexander Gelbukh",
note = "Publisher Copyright: {\textcopyright} 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.; 14th International Workshops on Semantic Evaluation, SemEval 2020 ; Conference date: 12-12-2020 Through 13-12-2020",
year = "2020",
language = "Ingl{\'e}s",
series = "14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings",
publisher = "International Committee for Computational Linguistics",
pages = "957--962",
editor = "Aurelie Herbelot and Xiaodan Zhu and Alexis Palmer and Nathan Schneider and Jonathan May and Ekaterina Shutova",
booktitle = "14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings",
}