Date of Award
Master of Science in Operations Research
Department of Operational Sciences
Christopher M. Smith, PhD.
The rapid expansion and acceptance of social media has opened doors into users’ opinions and perceptions that were never as accessible as they are with today's prevalence of mobile technology. Harvested data, analyzed for opinions and sentiment can provide powerful insight into a population. This research utilizes Twitter data due to its widespread global use, in order to examine the sentiment associated with tweets. An approach utilizing Twitter #hashtags and Latent Dirichlet Allocation topic modeling were utilized to differentiate between tweet topics. A lexicographical dictionary was then utilized to classify sentiment. This method provides a framework for an analyst to ingest Twitter data, conduct an analysis and provide insight into the sentiment contained within the data.
DTIC Accession Number
Munson, Evan L., "Sentiment Analysis of Twitter Data" (2018). Theses and Dissertations. 1853.