Data Dialogue
Friday, February 19, 2021, 12:00pm, None
Donald Pepka (Daisy Zhan, Chris Huebner, and Andrew Scofield)
Machine Learning and the Consumer Revolution
Abstract:
Machine Learning techniques, which have primarily been used in technological contexts, can quantify, confirm, and explore new questions in the Humanities. In this project, we've developed a framework to use the newest Machine Learning techniques to explore and confirm historical analyses by quantifying semantic shifts in the word "consumption," as well as sexist and antisemitic tropes. "In particular, we performed this analysis using Word Embedding models. Word Embeddings are a Machine Learning technology developed for industry to analyze customer data and create tools like AutoComplete. By representing words and phrases as vectors, we can use Word Embeddings to quantify the relationship between words over time. Since words can be defined in terms of their relationships to other words, this model provides a way to quantify changes in a word's meaning. Using newly developed methods for analyzing word embeddings, we track the development of the meanings of words related to consumerism, including their relationships with gender and antisemitism over time. Please register in advance for this event: https://duke.zoom.us/j/96797492251

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