Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter

Alon Rozental, Daniel Fleischer

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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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S17-2108.pdf
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