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/ingest-acl-2023-videos/S17-2108.pdf