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

Alon Rozental, Daniel Fleischer


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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
653–658
Language:
URL:
https://www.aclweb.org/anthology/S17-2108
DOI:
10.18653/v1/S17-2108
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2108.pdf