Abstract
This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3%. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations.- Anthology ID:
- S17-2135
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 802–806
- Language:
- URL:
- https://aclanthology.org/S17-2135
- DOI:
- 10.18653/v1/S17-2135
- Cite (ACL):
- Amit Ajit Deshmane and Jasper Friedrichs. 2017. TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 802–806, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis (Deshmane & Friedrichs, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2135.pdf