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).
@article{arxiv.1705.01306,
title = {Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter},
author = {Alon Rozental and Daniel Fleischer},
journal= {arXiv preprint arXiv:1705.01306},
year = {2018}
}
Comments
6 pages, accepted to the 11th International Workshop on Semantic Evaluation (SemEval-2017)