Emotion Recognition in Conversations (ERC) is a critical aspect of affective computing, and it has many practical applications in healthcare, education, chatbots, and social media platforms. Earlier approaches for ERC analysis involved modeling both speaker and long-term contextual information using graph neural network architectures. However, it is ideal to deploy speaker-independent models for real-world applications. Additionally, long context windows can potentially create confusion in recognizing the emotion of an utterance in a conversation. To overcome these limitations, we propose novel line conversation graph convolutional network (LineConGCN) and graph attention (LineConGAT) models for ERC analysis. These models are speaker-independent and built using a graph construction strategy for conversations -- line conversation graphs (LineConGraphs). The conversational context in LineConGraphs is short-term -- limited to one previous and future utterance, and speaker information is not part of the graph. We evaluate the performance of our proposed models on two benchmark datasets, IEMOCAP and MELD, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of 64.58% and 76.50%. Moreover, we demonstrate that embedding sentiment shift information into line conversation graphs further enhances the ERC performance in the case of GCN models.

情感识别对话（ERC）是情感计算的重要方面，它在医疗保健、教育、聊天机器人和社交媒体平台等方面具有许多实际应用。我们提出了适用于ERC分析的新型线性对话图卷积网络（LineConGCN）和图注意力（LineConGAT）模型，并评估了模型在两个基准数据集上的表现，证明了LineConGAT模型在F1得分为64.58％和76.50％的情况下优于最先进的方法。此外，我们还证明将情感变化信息嵌入到对话图中进一步提高了GCN模型的ERC性能。

LineConGraphs：利用图神经网络进行有效情感识别的线性对话图