Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information

Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information

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 p…

Yoga-Veganism: Correlation Mining of Twitter Health Data

Nowadays social media is a huge platform of data. People usually share their interest, thoughts via discussions, tweets, status. It is not possible to go through all the data manually. We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people's interest through the discussions. In this work, we explore Twitter data related to health. We extract the popular topics under different categories (e.g. diet, exercise) discussed in Twitter via topic modeling, observe model behavior on new tweets, discover interesting correlat…

使用弱监督图嵌入的Twitter用户表示

Twitter User Representation Using Weakly Supervised Graph Embedding

本文提出基于弱监督图嵌入的框架，通过Twitter上生活方式相关推文学习用户表示，有效检测用户类型并优于基线，可推广至其他领域。

使用联合嵌入模型分析Twitter用户的生活方式选择

Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

本文提出一种结合社交和文本信息的联合嵌入模型，用于学习情境化用户表示，以分析Twitter用户在瑜伽和生酮饮食等活动中的生活方式选择及动机，实验证明该模型在两个领域均提升了性能。

BERTuit: Understanding Spanish language in Twitter through a native transformer

The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever-changing stream of information written with informal and complex language, where each message requires careful evaluation to be understood even by humans given the important role that context plays. Addressing tasks in this domain through Natural Language Processing involves severe ch…

User-level sentiment analysis incorporating social networks

We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring…