Social and Emotional Correlates of Capitalization on Twitter
Social media text is replete with unusual capitalization patterns. We posit that capitalizing a token like THIS performs two expressive functions: it marks a person socially, and marks certain parts of an utterance as more salient than others. Focusing on gender and sentiment, we illustrate using a corpus of tweets that capitalization appears in more negative than positive contexts, and is used more by females compared to males. Yet we find that both genders use capitalization in a similar way when expressing sentiment.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisSimilar Papers 제목 키워드 기반
Non-lexical Features Encode Political Affiliation on Twitter
Previous work on classifying Twitter users{'} political alignment has mainly focused on lexical and social network features. This study provides evidence that political affiliation is also reflected in features which hav…
Emoji Usage Across Platforms: A Case Study for the Charlottesville Event
We study emoji usage patterns across two social media platforms, one of them considered a fringe community called Gab, and the other Twitter. We find that Gab tends to comparatively use more emotionally charged emoji, bu…
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…
Transfer LearningAffective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?
The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, On-line Support Groups (OSGs) can als…
Understanding and Measuring Psychological Stress using Social Media
A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how p…
Domain Adaptation