Date Thesis Awarded
5-2022
Access Type
Honors Thesis -- Access Restricted On-Campus Only
Degree Name
Bachelors of Science (BS)
Department
Mathematics
Advisor
Greg Hunt
Committee Members
Rex Kincaid
Heather Sasinowska
Jerry Watkins III
Abstract
Social media data has recently been looked to as a source of public opinion for elections, public policies, and the economy. In order to use this data effectively, natural language processing (NLP) techniques have been developed. Topic modeling, one branch of NLP, works to uncover latent topics with a large collection of tweets. Many topics modeling methods such as LDA and k-medoids clustering are unsupervised. We propose adding a supervised Random Forest layer before performing topic modeling in order to incorporate external knowledge. We find that implementing this layer helps increase the interpretability of topics as well as uncover unique topics. Sentiment analysis, another branch of NLP, measures the polarity of a tweet in order to gain insight into the author’s opinions. We apply several sentiment analysis methods to our dataset and examine the results; we identify weaknesses in these methods and propose steps for improvement.
Recommended Citation
Smith, Grace, "Investigating Text Mining Techniques Within the Context of Politicized Social Media Data" (2022). Undergraduate Honors Theses. William & Mary. Paper 1822.
https://scholarworks.wm.edu/honorstheses/1822
