@inproceedings{rozental-fleischer-2017-amobee,
title = "{A}mobee at {S}em{E}val-2017 Task 4: Deep Learning System for Sentiment Detection on {T}witter",
author = "Rozental, Alon and
Fleischer, Daniel",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2108",
doi = "10.18653/v1/S17-2108",
pages = "653--658",
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).",
}
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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).</abstract>
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%0 Conference Proceedings
%T Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter
%A Rozental, Alon
%A Fleischer, Daniel
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rozental-fleischer-2017-amobee
%X 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).
%R 10.18653/v1/S17-2108
%U https://aclanthology.org/S17-2108
%U https://doi.org/10.18653/v1/S17-2108
%P 653-658
Markdown (Informal)
[Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter](https://aclanthology.org/S17-2108) (Rozental & Fleischer, SemEval 2017)
ACL