论文标题
iust在Semeval-2020任务9：使用深神经网络和线性基线的代码混合社交媒体文本的情感分析
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines
论文作者
论文摘要
情感分析是自然语言处理的一个充分研究的领域。但是，其中社交媒体和嘈杂内容的快速增长在解决这个问题的方法和工具方面提出了重大挑战。这些挑战之一是混合代码，这意味着使用不同的语言在社交媒体文本中传达思想。我们的小组以iust（用户名：taha）的名字参加了Semeval-2020共享任务9的《代码混合社交媒体文本的情感分析》，我们试图开发一个系统来预测给定代码混合推文的情感。我们使用了不同的预处理技术，并建议使用不同方法，这些方法从NBSVM到更复杂的深度神经网络模型。我们最佳性能的方法获得了西班牙语 - 英语子任务的F1分数为0.751，而印度英语子任务的F1得分为0.706。
Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools. One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts. Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet. We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models. Our best performing method obtains an F1 score of 0.751 for the Spanish-English sub-task and 0.706 over the Hindi-English sub-task.
