Vocabulary Comparison of Medical School Applications Open Access Deposited
Date Modified: 05/14/2021
W2V takes terms from a large corpus of text and models them onto a vector space, based on word associations from your dataset. These Word Associations take into account each word's immediate context (its ten neighboring words).
Following the data modeling (large-scale unstructured text), The platform then generates a visualization of this vector space, which lets us perform analysis e.g. detect synonymous/synonym-ish words and highlight related words. At the heart of this project, is W2V's ability to identify key words that were more frequent - and more unique - to each group using results from 2 different W2V models – one for each group's application texts.
We coded these Key Terms into categories, then analyzed those categories for overarching themes.
- Geographic Subject
- Time Period
- In Collection:
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