This study thoroughly examines various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, with more than 22% in the F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to handle these complexities effectively. These findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT

Online social networks are the breeding grounds for user connections, fostering information exchange, communication, content sharing, and community building. However, the dissolution of these digital relationships, often a less-explored facet, complements the studies of tie formation and maintenance. A comprehensive grasp of these connections, encompassing their inception, unraveling, and the potential foresight of disconnections, offers invaluable insights into network dynamics and the progression of interpersonal bonds. Yet, the investigation of broken ties faces a substantial challenge: the paucity of longitudinal and detailed data. To bridge this gap, this paper curates an expansive dataset, spanning over 120,000 Twitter users tracked across 15 weeks with weekly snapshots. Armed with this dataset, we embark on an extensive exploration of Twitter links, delving into five distinct categories within the Twitter social graph. These categories encompass structural features like centrality, content-related aspects, including post polarity, user profile attributes like verified status, egocentric network elements such as reciprocity, and dense user representations typified by node2vec. Subsequently, we conduct a thorough analysis of these diverse features to unveil meaningful patterns.