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Full-Text Articles in Physical Sciences and Mathematics
Predictive Ai For The S&P 500 Index, Jacqueline Rose Perry
Predictive Ai For The S&P 500 Index, Jacqueline Rose Perry
Computer Science Senior Theses
Artificial intelligence has powerful applications in virtually every field, and the financial world is no exception. Utilizing various elements of artificial intelligence, this research aims to predict the future value of the S&P 500 index using numerous models, and in doing so, identify relevant features. More specifically, models that include combinations of historical data, public sentiment, and technical indicators were employed to predict the stock price one day and three days forward. To account for public opinion, the sentiment of tweets and news headlines from the beginning of 2015 through the end of 2019 was calculated using FinBERT, a pre-trained …
Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko
Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko
Computer Science Senior Theses
Chronic pain is a widespread problem that significantly impacts quality of life. Overprescription and abuse of pain medication continues to be a major public health issue and can further burden patients due to a fragmented health care system. Previous research has suggested a possible psychological basis to pain and the potential for safer, non-pharmacological alternatives for pain relief. This project leverages language models to study chronic pain development and relief through psychological treatments, which will be assessed through responses to post-treatment interviews. A transformer-based natural language processing model is employed to identify connections between language expressions and pain on a …
Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong
Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong
Dartmouth College Master’s Theses
When deciding on what news stories to cover, traditional journalism determines news values by following several elements of newsworthiness, such as impact, timeliness, and prominence. However, these guidelines do not always seem to correspond with the success of content on social media. As people are increasingly turning to social media for news, our research aims to understand and predict factors that drive user engagement for news on social media. In this study, we analyze news content published on Twitter, and examine a diverse set of characteristics like metrics retrieved from the Twitter API and semantics by natural language processing, including …
Leveraging Context Patterns For Medical Entity Classification, Garrett Johnston
Leveraging Context Patterns For Medical Entity Classification, Garrett Johnston
Computer Science Senior Theses
The ability of patients to understand health-related text is important for optimal health outcomes. A system that can automatically annotate medical entities could help patients better understand health-related text. Such a system would also accelerate manual data annotation for this low-resource domain as well as assist in down- stream medical NLP tasks such as finding textual similarity, identifying conflicting medical advice, and aspect-based sentiment analysis. In this work, we investigate a state-of-the-art entity set expansion model, BootstrapNet, for the task of medical entity classification on a new dataset of medical advice text. We also propose EP SBERT, a simple model …
Identifying Optimal Course Structures Using Topic Models, Tehut Tesfaye Biru
Identifying Optimal Course Structures Using Topic Models, Tehut Tesfaye Biru
Dartmouth College Undergraduate Theses
This research project investigates whether there exists an optimal way to structure topics in educational course content that results in higher levels of engagement among students. It is implemented by fitting topic models to transcripts of educational videos contained in the Khan Academy platform. The fitted models were used to extract topic trajectories across time for each video and subsequently clustered based on whether they have similar “shapes”. The differences in mean engagement metrics per cluster suggest that some course shapes are more palatable to students regardless of subject matter. Additionally, the topic trajectories suggest a constant progression of topics …