School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Math Biosci Eng. 2022 Jan 10;19(3):2671-2699. doi: 10.3934/mbe.2022122.
With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an is constructed, which represents the original conversation-text as an n-dimensional so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.
随着在线社交网络的快速发展，文本通信已成为日常生活中不可或缺的一部分。挖掘对话文本背后隐藏的情感，对于政府舆论监督、企业决策等具有重要的意义和应用价值。因此，在本文中，我们提出了一种多参与者文本对话场景下的文本情感预测模型，旨在有效地预测目标说话者未来发布的文本的情感。具体来说，首先构建一个 ，将原始对话文本表示为一个 n 维 ，以便获得不同情感类别下的文本表示。其次，采用类似场景搜索机制，寻找几个包含与当前对话场景相似情感变化趋势的子序列。最后，构建一个具有情感融合和混合注意力机制的两层编码器-解码器结构的文本情感预测模型，分别在编码器和解码器侧引入情感融合和混合注意力机制。根据实验结果，我们提出的模型由于从相似场景中提取了辅助特征，以及采用了情感融合和混合注意力机制，因此可以在情感预测方面取得整体最佳性能。同时，预测效率仍可控制在可接受的水平。
