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Our system, VISU, participated in the WASSA 2023 Shared Task (3) of EmotionClassification from essays written in reaction to news articles. Emotiondetection from complex dialogues is challenging and often requirescontext/domain understanding. Therefore in this research, we have focused ondeveloping deep learning (DL) models using the combination of word embeddingrepresentations with tailored prepossessing strategies to capture the nuancesof emotions expressed. Our experiments used static and contextual embeddings(individual and stacked) with Bidirectional Long short-term memory (BiLSTM) andTransformer based models. We occupied rank tenth in the emotion detection taskby scoring a Macro F1-Score of 0.2717, validating the efficacy of ourimplemented approaches for small and imbalanced datasets with mixed categoriesof target emotions.

VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings