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arXiv · 2009.03397

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
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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.

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Jason Angel, Segun Taofeek Aroyehun, Antonio Tamayo, Alexander Gelbukh. 2020-09-07. NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier. https://arxiv.org/abs/2009.03397

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