Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information
arXiv:2012.02939 · doi:10.1109/BigData50022.2020.9378461
Abstract
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".
arXiv commentaccepted at IEEE BigData 2020
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- Efficient Estimation of Word Representations in Vector Space
- Neural Machine Translation by Jointly Learning to Align and Translate
- Decoupled Weight Decay Regularization
- ADADELTA: An Adaptive Learning Rate Method
- emoji2vec: Learning Emoji Representations from their Description
- Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
- Quantifying Mental Health from Social Media with Neural User Embeddings
- Yoga-Veganism: Correlation Mining of Twitter Health Data
- Detecting and Explaining Causes From Text For a Time Series Event
- You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP