Abstract
This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6%.- Anthology ID:
- S17-2128
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 760–765
- Language:
- URL:
- https://aclanthology.org/S17-2128
- DOI:
- 10.18653/v1/S17-2128
- Cite (ACL):
- Mickael Rouvier. 2017. LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 760–765, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification (Rouvier, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2128.pdf