Code-mixing is a frequently observed phenomenon in multilingual communities where a speaker uses multiple languages in an utterance or sentence. Code-mixed texts are abundant, especially in social media, and pose a problem for NLP tools as they are typically trained on monolingual corpora. Recently, finding the sentiment from code-mixed text has been attempted by some researchers in SentiMix SemEval 2020 and Dravidian-CodeMix FIRE 2020 shared tasks. Mostly, the attempts include traditional methods, long short term memory, convolutional neural networks, and transformer models for code-mixed sentiment analysis (CMSA). However, no study has explored graph convolutional neural networks on CMSA. In this paper, we propose the graph convolutional networks (GCN) for sentiment analysis on code-mixed text. We have used the datasets from the Dravidian-CodeMix FIRE 2020. Our experimental results on multiple CMSA datasets demonstrate that the GCN with multi-headed attention model has shown an improvement in classification metrics.
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机译：代码混合是多语种社区中经常观察到的现象，其中扬声器在话语或句子中使用多种语言。代码混合文本丰富，特别是在社交媒体上，对NLP工具构成问题，因为它们通常在单机语料库上培训。最近，在Sentimix Semeval 2020和Dravidian-Codemix Fire 2020共享任务中尝试了从Code-Mixion文本中找到的情绪。主要是，该尝试包括传统方法，长期内存，卷积神经网络和用于代码混合情感分析的变压器模型（CMSA）。然而，没有研究在CMSA上探索了图形卷积神经网络。在本文中，我们提出了关于代码混合文本的情感分析的图表卷积网络（GCN）。我们已经使用了Dravidian-Codemix Fire 2020的数据集。我们在多个CMSA数据集上的实验结果表明，具有多头关注模型的GCN已经显示出分类度量的改进。
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