EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets

TL;DR

This paper presents an ensemble system combining N-Stream ConvNets and XGBoost regressors for emotion analysis in tweets, achieving top performance in Arabic valence tasks at SemEval-2018.

Contribution

It introduces a novel ensemble approach integrating deep learning and gradient boosting for emotion analysis in tweets, specifically targeting Arabic language tasks.

Findings

Outperformed all other approaches in Arabic valence intensity regression.

Achieved top results in valence ordinal classification.

Effective combination of deep learning and feature-based models.

Abstract

This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regresseor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task.