@inproceedings{dowlagar-mamidi-2021-graph,
    title = "Graph Convolutional Networks with Multi-headed Attention for Code-Mixed Sentiment Analysis",
    author = "Dowlagar, Suman  and
      Mamidi, Radhika",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Kumar M, Anand  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.dravidianlangtech-1.8/",
    pages = "65--72",
    abstract = "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."
}