Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter
Abstract
This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).- Anthology ID:
- S17-2108
- 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:
- 653–658
- Language:
- URL:
- https://aclanthology.org/S17-2108
- DOI:
- 10.18653/v1/S17-2108
- Cite (ACL):
- Alon Rozental and Daniel Fleischer. 2017. Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 653–658, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter (Rozental & Fleischer, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S17-2108.pdf