Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions.

翻译：我们的系统VISU参与了WASSA 2023共享任务（三）：基于新闻文章反应性短文的情感分类。从复杂对话中检测情感具有挑战性，通常需要上下文/领域理解。因此，本研究聚焦于开发结合词嵌入表示与定制预处理策略的深度学习（DL）模型，以捕捉情感表达的细微差别。实验采用静态嵌入与上下文嵌入（单独及堆叠），结合双向长短期记忆网络（BiLSTM）及基于Transformer的模型。通过在小规模、不平衡且目标情感类别混杂的数据集上获得0.2717的宏F1分数，我们在情感检测任务中位列第十，验证了所提方法的有效性。