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

## TL;DR

This paper presents the Amobee system for Twitter sentiment analysis, combining RNNs and traditional classifiers, achieving third place in a SemEval 2017 challenge.

## Contribution

It introduces a hybrid approach integrating deep learning and classical classifiers for improved Twitter sentiment detection.

## Key findings

- Achieved 3rd place in SemEval-2017 sub-task C
- Combined RNN with Naive Bayes and logistic regression
- Demonstrated effectiveness of hybrid models for sentiment analysis

## 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).