Daily dialogues are full of emotions that control the trends of dialogues and influence the attitudes of interlocutors toward each other, and understanding the human emotions in dialogues is of great significance in emotional comfort, human–computer interaction and intelligent question-answering. This paper defines a new task called emotion prediction in textual dialogue. Different from the text emotion recognition task, which derives the current emotional state of interlocutor from the utterance, emotion prediction aims at predicting the future emotional state of interlocutor before the interlocutor utters something. Moreover, this paper summarizes and explains three notable characteristics of emotional propagation in text dialogue: context dependence, persistence and contagiousness. By considering these characteristics, a fully data-driven interactive double states emotion cell model (IDS-ECM) is proposed. The model has two layers. The first layer automatically extracts the emotional information of historical dialogue and is used to describe the contextual dependence of the textual dialogue emotion. The second layer models the change process of interlocutors’ emotional states during the dialogue and depicts the persistence and contagiousness of emotions. Experimental results on two manually annotated datasets show that the proposed model is superior to the baseline in the macro-averaged F1 evaluation metric and that the proposed model can simulate the emotional changes in the process of dialogue so as to predict the emotions with high accuracy. The experimental results also reveal the communication differences between different emotional categories in dialogue, which is of guiding significance for future research.

日常对话充满了控制对话趋势并影响对话者彼此态度的情感，理解对话中的人类情感对情感舒适，人机交互和智能问答具有重要意义。本文定义了一项新任务，称为文本对话中的情感预测。与文本情感识别任务不同，文本情感识别任务是从话语中得出对话者当前的情感状态，情感预测的目的是在对话者说出某些东西之前预测对话者的未来情感状态。此外，本文总结并解释了文本对话中情感传播的三个显着特征：上下文依赖，持久性和传染性。考虑到这些特征，提出了一种完全数据驱动的交互式双态情绪细胞模型（IDS-ECM）。该模型有两层。第一层自动提取历史对话的情感信息，并用于描述文本对话情感的上下文相关性。第二层模拟对话期间对话者情绪状态的变化过程，并描述情绪的持久性和传染性。在两个手动注释的数据集上的实验结果表明，该模型在宏观平均F1评估指标中优于基线，并且该模型可以模拟对话过程中的情绪变化，从而可以高精度地预测情绪。实验结果还揭示了对话中不同情感类别之间的交流差异，