  The Music Emotion Recognition (MER) field has seen steady developments in
recent years, with contributions from feature engineering, machine learning,
and deep learning. The landscape has also shifted from audio-centric systems to
bimodal ensembles that combine audio and lyrics. However, a severe lack of
public and sizeable bimodal databases has hampered the development and
improvement of bimodal audio-lyrics systems. This article proposes three new
audio, lyrics, and bimodal MER research datasets, collectively called MERGE,
created using a semi-automatic approach. To comprehensively assess the proposed
datasets and establish a baseline for benchmarking, we conducted several
experiments for each modality, using feature engineering, machine learning, and
deep learning methodologies. In addition, we propose and validate fixed
train-validate-test splits. The obtained results confirm the viability of the
proposed datasets, achieving the best overall result of 79.21% F1-score for
bimodal classification using a deep neural network.
