  Emotion recognition in conversation, which aims to predict the emotion for
all utterances, has attracted considerable research attention in recent years.
It is a challenging task since the recognition of the emotion in one utterance
involves many complex factors, such as the conversational context, the
speaker's background, and the subtle difference between emotion labels. In this
paper, we propose a novel framework which mimics the thinking process when
modeling these factors. Specifically, we first comprehend the conversational
context with a history-oriented prompt to selectively gather information from
predecessors of the target utterance. We then model the speaker's background
with an experience-oriented prompt to retrieve the similar utterances from all
conversations. We finally differentiate the subtle label semantics with a
paraphrasing mechanism to elicit the intrinsic label related knowledge. We
conducted extensive experiments on three benchmarks. The empirical results
demonstrate the superiority of our proposed framework over the state-of-the-art
baselines.
