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Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Twitter Sentiment Analysis: Applications In Healthcare And Finance, Jiali Wang
Twitter Sentiment Analysis: Applications In Healthcare And Finance, Jiali Wang
Dissertations
This research explores the influence of Twitter sentiment on healthcare and finance industries. It assesses how Twitter sentiment and culture measure influence COVID-19 statistics, and it investigates the impact of Twitter sentiment on S&P 1500 stock mispricing. Furthermore, it examines how tweet sentiment predicts major industry returns.
The first part examines how Hofstede’s Culture Dimensions (HCD) and Twitter economic uncertainty index (TEU) relate to COVID-19 infection rate and death rate. The results show certain aspects in HCD, such as power distance index (PDI) and masculinity (MAS) both are negatively and significantly associated with the infection rate, while indulgence (IVR) and …
Analyzing Fluctuation Of Topics And Public Sentiment Through Social Media Data, Haoyue Liu
Analyzing Fluctuation Of Topics And Public Sentiment Through Social Media Data, Haoyue Liu
Dissertations
Over the past decade years, Internet users were expending rapidly in the world. They form various online social networks through such Internet platforms as Twitter, Facebook and Instagram. These platforms provide a fast way that helps their users receive and disseminate information and express personal opinions in virtual space. When dealing with massive and chaotic social media data, how to accurately determine what events or concepts users are discussing is an interesting and important problem.
This dissertation work mainly consists of two parts. First, this research pays attention to mining the hidden topics and user interest trend by analyzing real-world …
Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu
Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu
Dissertations
During the past decade, drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drugabuse risk behavior at a population scale, such as among the population of Twitter users, can help to monitor the trend of drugabuse incidents. However, traditional methods do not effectively detect drugabuse risk behavior in tweets, mainly due to the sparsity of such tweets and the noisy nature of tweets. In the first part of this dissertation work, the task of classifying tweets as containing drugabuse risk behavior or not, is studied. Millions of public …