Research Mentor(s): Dyanne Vaught, Ph.D. Candidate
Research Mentor School/College/Department: Economics, College of Literature, Science, and the Arts
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 19
The aim was to examine the changes in sentiment that occurred within trade agreements over time using rule-based sentiment analysis. In this case, the sentiment of a text was measured by the degree to which it expresses or implies an opinion. The focus was on a collection of English hundred trade agreements written within the last few decades. Before analysis, a dictionary of trade terms was created using seminal texts. Terms were categorized based on if they expressed a cooperative, punitive, or bureaucratic sentiment. The analysis entailed assigning sentiment scores to trade agreements based on the number of categorized terms within. There is no clear expected result, but there will likely be some observable change in overall sentiment over time, whether it is more or less sentiment.
Authors: Daniel Tafoya, Dyanne Vaugt
Research Method: Computer Programming