Twitter emotions
Emotion and sentiment analysis from Twitter text – ScienceDirect
efter K Sailunaz · 2019 · Citeret af 252 — We detect and analyze sentiment and emotion expressed by people from text in their twitter posts. •. We collected tweets and replies on few specific topics …
History of Emotions (@emotionshistory) / Twitter
Working on histories of memory and/or emotion? Our autumn call for papers is out now, send us an abstract by 16 September!
The psychology of shareable content – Twitter for Business
The psychology of shareable content
You might think that positive emotions, like joy or excitement, are the most likely to increase sharability—and you’re partially right. When readers experienced …
Emotion Review (@Emotion_Review) / Twitter
An interdisciplinary scholarly journal focusing on ideas about emotion. … Turn to Emotion Review for theory, critique, debate, and conceptual analysis …
Emotions 22-Tweet (@emotions22tweet) / Twitter
PSY223 Understanding and Managing Emotions Official Twitter Account. Sharing, discussing and tweeting emotions-related information.
Twitter, time and emotions | Royal Society Open Science
efter E Mayor · 2021 · Citeret af 17 — The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear …
Emotional Sharing on Social Media: How Twitter Replies …
We used this balanced sample to test for differences between the reply and original tweet. We quantified an approximation of the positive and negative emotions …
Politics of Twitter: Emotions and the Power of Social Media
efter C Duncombe · 2019 · Citeret af 89 — This article analyses the emotional dynamics of Twitter, illustrating how emotion is implicated in the power of this social media platform. I …
A practical guide to emotional sentiment analysis on Twitter
A practical guide to emotional sentiment analysis on Twitter | by Catalao Alves | iNOVAMedialab | Medium
In sentiment analysis, neutral tweets usually outnumber the negative or positive ones. This is what actually happened during the 6-year period before the online …
The ongoing competition for a viable coronavirus vaccine is arguably the race of the century. With its hundred of millions of users, Twitter is particularly well-suited for research into the…
Twitter Discussions and Emotions About the COVID-19 …
Journal of Medical Internet Research – Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
efter J Xue · 2020 · Citeret af 250 — Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology …
Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring.Objective: The objective of this study is to examine COVID-19–related discussions, concerns, and sentiments using tweets posted by Twitter users.Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets.Results: Popular unigrams included “virus,” “lockdown,” and “quarantine.” Popular bigrams included “COVID-19,” “stay home,” “corona virus,” “social distancing,” and “new cases.” We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics.Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
Keywords: twitter emotions
