论文标题
NLP-CIC在Semeval-2020任务9：使用简单的深度学习分类器分析代码转换语言的情感
NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier
论文作者
论文摘要
代码切换是一种现象，其中在同一消息中使用两种或多种语言。如今，找到与社交媒体混合的语言的消息非常普遍。这种现象提出了情感分析的挑战。在本文中，我们使用标准的卷积神经网络模型来预测西班牙语和英语语言融合的推文的情感。我们简单的方法在竞争中测试集的F1得分为0.71。我们分析最佳模型功能并执行错误分析，以暴露在代码转换设置中对情感分类的重要困难。
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.
