论文标题
瑜伽让你开心吗？使用文本和时间信息分析Twitter用户幸福
Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information
论文作者
论文摘要
尽管瑜伽是一种磨练身心并闻名以减少焦虑和抑郁的多组分实践，但了解人们在社交媒体中与瑜伽相关的情绪状态仍然存在差距。在这项研究中，我们通过使用Granger因果关系纳入用户的文本和时间信息来调查练习瑜伽与快乐之间的因果关系。为了找出文本中的因果特征，我们根据内容分析和（ii）基于情绪状态的幸福水平测量两个变量（i）瑜伽活动水平。为了了解用户的瑜伽活动，我们通过利用用户的社交和文本信息来基于神经网络与注意机制的融合，提出一个联合嵌入模型。为了衡量瑜伽用户（目标领域）的情绪状态，我们建议一种转移学习方法来转移知识从基于注意的神经网络模型进行培训的基于注意的神经网络模型。我们在Twitter数据集上的实验表明，有1447个用户“瑜伽Granger会导致幸福感”。
Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media. In this study, we investigate the causal relationship between practicing yoga and being happy by incorporating textual and temporal information of users using Granger causality. To find out causal features from the text, we measure two variables (i) Yoga activity level based on content analysis and (ii) Happiness level based on emotional state. To understand users' yoga activity, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by leveraging users' social and textual information. For measuring the emotional state of yoga users (target domain), we suggest a transfer learning approach to transfer knowledge from an attention-based neural network model trained on a source domain. Our experiment on Twitter dataset demonstrates that there are 1447 users where "yoga Granger-causes happiness".
