LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso
Abstract
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 HindiEnglish 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.- Anthology ID:
- 2020.semeval-1.172
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1281–1287
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.172
- DOI:
- 10.18653/v1/2020.semeval-1.172
- Cite (ACL):
- Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, and Paolo Rosso. 2020. LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1281–1287, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis (Banerjee et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.semeval-1.172.pdf
- Data
- SentiMix