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Full-Text Articles in Computer Sciences
Tracking Xenophobic Terminology On Twitter Using Nlp, Harper Lyon
Tracking Xenophobic Terminology On Twitter Using Nlp, Harper Lyon
Honors Theses
Social media is a major driver of political thought, with platforms like Facebook, Twitter, and TikTok having a massive impact on how people think and vote. For this reason we should take seriously any large shifts in the language used to describe issues or groups on social media, as these are likely to either denote a change in political thought or even forecast the same. Of particular interest, given the international reach of social media, is the way that discussions around foreign relations and immigration play out. In the United States of America online spaces have become the default space …
Using Large Pre-Trained Language Models To Track Emotions Of Cancer Patients On Twitter, Will Baker
Using Large Pre-Trained Language Models To Track Emotions Of Cancer Patients On Twitter, Will Baker
Computer Science and Computer Engineering Undergraduate Honors Theses
Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in …
Sentiment Analysis, Quantification, And Shift Detection, Kevin Labille
Sentiment Analysis, Quantification, And Shift Detection, Kevin Labille
Graduate Theses and Dissertations
This dissertation focuses on event detection within streams of Tweets based on sentiment quantification. Sentiment quantification extends sentiment analysis, the analysis of the sentiment of individual documents, to analyze the sentiment of an aggregated collection of documents. Although the former has been widely researched, the latter has drawn less attention but offers greater potential to enhance current business intelligence systems. Indeed, knowing the proportion of positive and negative Tweets is much more valuable than knowing which individual Tweets are positive or negative. We also extend our sentiment quantification research to analyze the evolution of sentiment over time to automatically detect …
An Evaluation Of Geotagged Twitter Data During Hurricane Irma Using Sentiment Analysis And Topic Modeling For Disaster Resilience, Ike Robert Vayansky
An Evaluation Of Geotagged Twitter Data During Hurricane Irma Using Sentiment Analysis And Topic Modeling For Disaster Resilience, Ike Robert Vayansky
Electronic Theses and Dissertations
Disasters require quick response times, thought-out preparations, overall community, and government support. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are …
Data Preparation For Social Network Mining And Analysis, Yazhe Wang
Data Preparation For Social Network Mining And Analysis, Yazhe Wang
Dissertations and Theses Collection (Open Access)
This dissertation studies the problem of preparing good-quality social network data for data analysis and mining. Modern online social networks such as Twitter, Facebook, and LinkedIn have rapidly grown in popularity. The consequent availability of a wealth of social network data provides an unprecedented opportunity for data analysis and mining researchers to determine useful and actionable information in a wide variety of fields such as social sciences, marketing, management, and security. However, raw social network data are vast, noisy, distributed, and sensitive in nature, which challenge data mining and analysis tasks in storage, efficiency, accuracy, etc. Many mining algorithms cannot …