Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes

TL;DR

This paper introduces a novel cluster-based deep ensemble model for emotion classification in memes, combining deep learning and clustering to improve accuracy and achieve state-of-the-art results.

Contribution

The paper presents a hybrid clustering and deep ensemble approach specifically designed for emotion classification in memes, outperforming existing models.

Findings

CDEL outperforms baseline models on benchmark datasets.

Clustering enhances emotion classification accuracy.

Component analysis confirms the effectiveness of the hybrid approach.

Abstract

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.