Angad Sandhu
MSE student at Johns Hopkins University. Working on advanced multimodal models at Johns Hopkins Medicine, building tools for the best doctors in the world.
Sessions
This talk presents our novel work on detecting changes in topics over time and visualizing the change in the meaning of words within a corpus. Utilizing cosine similarity from word embeddings, our method maps the convergence or divergence of topic clusters over time. Applied to a diverse text dataset, such as news articles, this approach provides deep insights into public discourse and information flow during major global events. We demonstrate this through sports data, COVID-19-related data, and nursing home reviews. This talk benefits anyone interested in understanding how words and languages change over time, particularly for data scientists who need an analysis tool for gauging insights into text data. No prior knowledge is required, as the talk will offer a high-level overview of the methodology through case studies. The presentation will consist of a 10-15 minute discussion of the paper's findings with various examples, followed by a 10-15 minute demonstration of visualizing word changes in various datasets through our Django web app.
Original paper: https://arxiv.org/abs/2209.11717