  We propose VADEC, a multi-task framework that exploits the correlation
between the categorical and dimensional models of emotion representation for
better subjectivity analysis. Focusing primarily on the effective detection of
emotions from tweets, we jointly train multi-label emotion classification and
multi-dimensional emotion regression, thereby utilizing the inter-relatedness
between the tasks. Co-training especially helps in improving the performance of
the classification task as we outperform the strongest baselines with 3.4%,
11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores
respectively on the AIT dataset. We also achieve state-of-the-art results with
11.3% gains averaged over six different metrics on the SenWave dataset. For the
regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5%
gains in Pearson Correlation scores over the current state-of-the-art on the
EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions
respectively. We conclude our work with a case study on COVID-19 tweets posted
by Indians that further helps in establishing the efficacy of our proposed
solution.
