This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix Hindi-English subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.
@article{arxiv.2008.13173,
title = {LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis},
author = {Somnath Banerjee and Sahar Ghannay and Sophie Rosset and Anne Vilnat and Paolo Rosso},
journal= {arXiv preprint arXiv:2008.13173},
year = {2020}
}
Comments
To be published in the Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020), Barcelona, Spain, Sep. Association for Computational Linguistics