  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.
