Title

Sentiment analysis and social media: quantifying the impact of the 2016 presidential election

Document Type

Poster

Event Website

https://source2022.sched.com/

Start Date

16-5-2022

End Date

16-5-2022

Keywords

Sentiment Analysis, Numerical Methods, Statistics

Abstract

Sentiment analysis, also known as opinion mining, is an application of natural language processing (NLP) in computer science that aims to identify positive or negative sentiments expressed in human languages. Sentiment analysis systems are given an excerpt of human-written text and are able to classify the author's sentiment as positive, negative, or neutral. As a repository for vast amounts of user-generated content, social media websites are naturally of great interest in sentiment analysis research, and many papers have been written on the topic. In this project, we use sentiment analysis tools to determine overall user sentiment in comments on Reddit's r/politics board, a forum for discussion of American politics, posted between the creation of the board in October 2007 and the most recent data in December 2021. We then use a variety of numerical and statistical methods to examine potential trends in overall user sentiment and explore the effect of recent election cycles on the sentiment of online political conversation, with a particular focus on the the short- and long- term effects of the 2016 presidential election.

Faculty Mentor(s)

Brandy Wiegers

Department/Program

Applied Mathematics

Additional Mentoring Department

Applied Mathematics

Ohara, Zach Source presentation.mp4 (53493 kB)
Video Presentation

Additional Files

Ohara, Zach Source presentation.mp4 (53493 kB)
Video Presentation

Share

COinS
 
May 16th, 12:00 AM May 16th, 12:00 AM

Sentiment analysis and social media: quantifying the impact of the 2016 presidential election

Sentiment analysis, also known as opinion mining, is an application of natural language processing (NLP) in computer science that aims to identify positive or negative sentiments expressed in human languages. Sentiment analysis systems are given an excerpt of human-written text and are able to classify the author's sentiment as positive, negative, or neutral. As a repository for vast amounts of user-generated content, social media websites are naturally of great interest in sentiment analysis research, and many papers have been written on the topic. In this project, we use sentiment analysis tools to determine overall user sentiment in comments on Reddit's r/politics board, a forum for discussion of American politics, posted between the creation of the board in October 2007 and the most recent data in December 2021. We then use a variety of numerical and statistical methods to examine potential trends in overall user sentiment and explore the effect of recent election cycles on the sentiment of online political conversation, with a particular focus on the the short- and long- term effects of the 2016 presidential election.

https://digitalcommons.cwu.edu/source/2022/COTS/83