Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India.
Department of Computer Science, Munster Technological University (Cork Campus), Cork, Ireland.
Sci Rep. 2022 Jan 11;12(1):493. doi: 10.1038/s41598-021-04331-3.
Temporal orientation is an important aspect of human cognition which shows how an individual emphasizes past, present, and future. Theoretical research in psychology shows that one's emotional state can influence his/her temporal orientation. We hypothesize that measuring human temporal orientation can benefit from concurrent learning of emotion. To test this hypothesis, we propose a deep learning-based multi-task framework where we concurrently learn a unified model for temporal orientation (our primary task) and emotion analysis (secondary task) using tweets. Our multi-task framework takes users' tweets as input and produces three temporal orientation labels (past, present or future) and four emotion labels (joy, sadness, anger, or fear) with intensity values as outputs. The classified tweets are then grouped for each user to obtain the user-level temporal orientation and emotion. Finally, we investigate the associations between the users' temporal orientation and their emotional state. Our analysis reveals that joy and anger are correlated to future orientation while sadness and fear are correlated to the past orientation.
时间取向是人类认知的一个重要方面，它展示了个体如何强调过去、现在和未来。心理学的理论研究表明，一个人的情绪状态会影响他/她的时间取向。我们假设，通过同时学习情绪，人类的时间取向可以得到改善。为了验证这一假设，我们提出了一种基于深度学习的多任务框架，该框架使用推文同时学习时间取向（主要任务）和情绪分析（辅助任务）的统一模型。我们的多任务框架以用户的推文作为输入，输出三个时间取向标签（过去、现在或未来）和四个情绪标签（喜悦、悲伤、愤怒或恐惧）及其强度值。然后，对分类推文进行分组，以获取用户级别的时间取向和情绪。最后，我们研究了用户的时间取向与其情绪状态之间的关联。我们的分析表明，喜悦和愤怒与未来取向相关，而悲伤和恐惧与过去取向相关。
