Johns Hopkins University 3 credits Computer Science
[Alt. title: Analyzing Text as Data] Vastly available digitized text data has created new opportunities for understanding social phenomena. Relatedly, social issues like toxicity, discrimination, and propaganda frequently manifest in text, making text analyses critical for understanding and mitigating them. In this course, we will centrally explore: how can we use NLP as a tool for understanding society? Students will learn core and recent advances in text-analysis methodology, building from word-level metrics to embeddings and language models as well as incorporating statistical methods such as time series analyses and causal inference. Required Course Background: natural language processing and familiarity with Python/PyTorch. Students may receive credit for EN.601.472 or EN.601.672, but not both. Prerequisite(s): Students can only receive credit for one of the following: EN.601.472 OR EN.601.672.
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