  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).
