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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 …
Entity Based Sentiment Analysis For Textual Health Advice, Dae Lim Chung
Entity Based Sentiment Analysis For Textual Health Advice, Dae Lim Chung
Computer Science Senior Theses
This work explores entity based sentiment analysis for textual health advice through deep learning. We fine tuned a pretrained BERT model to analyze sentiments across five different predetermined categories which consist of food, medicine, disease, exercise, and vitality for three different sentiments: positive, negative, and neutral. Original set of annotated medical dataset from Dartmouth College’s Persist Lab was used to conduct the experiments. For the aim of tailoring the data for the purpose of entity based sentiment analysis, we explored data transformation techniques to generate optimum training examples. During the experiments, we were able to discover that the wide variety …
Interpreting Attention-Based Models For Natural Language Processing, Steven J. Signorelli Jr
Interpreting Attention-Based Models For Natural Language Processing, Steven J. Signorelli Jr
Dartmouth College Undergraduate Theses
Large pre-trained language models (PLMs) such as BERT and XLNet have revolutionized the field of natural language processing (NLP). The interesting thing is that they are pre- trained through unsupervised tasks, so there is a natural curiosity as to what linguistic knowledge these models have learned from only unlabeled data. Fortunately, these models’ architectures are based on self-attention mechanisms, which are naturally interpretable. As such, there is a growing body of work that uses attention to gain insight as to what linguistic knowledge is possessed by these models. Most attention-focused studies use BERT as their subject, and consequently the field …