College of Computer, National University of Defense Technology, Changsha 410073, China.
School of Software & Microelectronics, Peking University, Beijing 100191, China.
Sensors (Basel). 2022 Nov 3;22(21):8450. doi: 10.3390/s22218450.
Emotional tracking on time-varying virtual space communication aims to identify sentiments and opinions expressed in a piece of user-generated content. However, the existing research mainly focuses on the user's single post, despite the fact that social network data are sequential. In this article, we propose a sentiment analysis model based on time series prediction in order to understand and master the chronological evolution of the user's point of view. Specifically, with the help of a domain-knowledge-enhanced pre-trained encoder, the model embeds tokens for each moment in the text sequence. We then propose an attention-based temporal prediction model to extract rich timing information from historical posting records, which improves the prediction of the user's current state and personalizes the analysis of user's sentiment changes in social networks. The experiments show that the proposed model improves on four kinds of sentiment tasks and significantly outperforms the strong baseline.
情感跟踪随时间变化的虚拟空间通信旨在识别用户生成内容中表达的情感和意见。然而，现有的研究主要集中在用户的单个帖子上，尽管社交网络数据是序列的。在本文中，我们提出了一种基于时间序列预测的情感分析模型，以便理解和掌握用户观点的时间演变。具体来说，借助领域知识增强的预训练编码器，该模型为文本序列中的每个时刻嵌入标记。然后，我们提出了一种基于注意力的时间预测模型，从历史发布记录中提取丰富的时间信息，从而提高对用户当前状态的预测，并个性化分析社交网络中用户情感的变化。实验表明，所提出的模型在四种情感任务上都有所改进，并且明显优于强大的基线。
