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.

我们的系统 VISU 参与了 WASSA 2023 共享任务（3），对以新闻文章为反应的论文进行情感分类。通过深度学习模型使用单词嵌入表示与定制的预处理策略相结合，我们专注于捕捉情感表达的细微差异。我们的实验使用静态和上下文嵌入（个体和堆叠）与双向长短时记忆（BiLSTM）和变换器模型。在情感检测任务中，我们以 0.2717 的宏 F1 得分排名第十，验证了我们实现的方法在小型和不平衡的混合目标情感数据集上的有效性。