Different from the emotion estimation in individual utterances,
context-sensitive and speaker-sensitive dependences are vitally
pivotal for conversational emotion analysis. In this paper, we
propose a graph-based neural network to model these dependences. Specifically, our approach represents each utterance
and each speaker as a node. To bridge the context-sensitive
dependence, each utterance node has edges between immediate
utterances from the same conversation. Meanwhile, the directed
edges between each utterance node and its speaker node bridge
the speaker-sensitive dependence. To verify the effectiveness
of our strategy, we conduct experiments on the MELD dataset.
Experimental results demonstrate that our method shows an absolute improvement of 1%∼2% over state-of-the-art strategies.