  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.
