Dataset

 

Vocabulary Comparison of Medical School Applications Open Access Deposited

Default
Date Uploaded: 05/14/2021
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.​

Creator
License
Subject
Geographic Subject
Time Period
  • 2010-2019
Submitter
College
Department
Publisher
Language

Relationships

In Collection:

Items

Permanent link to this page: https://scholar.uc.edu/show/kw52j9497