Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics.
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19.
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
We examined temporal patterns of the linguistic expression of individuals on Chinese social media (Weibo).
By using the NaiveBayes, RandomForests, libSVM and SMO classification, the recognition rate of natural and unnatural emotions can reach above 70%. It is concluded that using the Kinect system can be a new method in recognition of emotions.
Emotion identification from gait aims to automatically determine persons affective state, it has attracted a great deal of interests and offered immense potential value in action tendency, health care, psychological detection and human-computer(robot) interaction. In this paper, we propose a new method of identifying emotion from natural walking, and analyze the relevance between the traits of walking and affective states.
If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives.
Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model.
Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media.