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

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).

arXiv comment6 pages, accepted to the 11th International Workshop on Semantic Evaluation (SemEval-2017)