Graduation Year

2016

Date of Submission

4-2016

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

Mathematical Sciences

Reader 1

Blake Hunter

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2016 Chong Shen

Abstract

The ongoing European Refugee Crisis has been one of the most popular trending topics on Twitter for the past 8 months. This paper applies topic modeling on bulks of tweets to discover the hidden patterns within these social media discussions. In particular, we perform topic analysis through solving Non-negative Matrix Factorization (NMF) as an Inexact Alternating Least Squares problem. We accelerate the computation using techniques including tweet sampling and augmented NMF, compare NMF results with different ranks and visualize the outputs through topic representation and frequency plots. We observe that supportive sentiments maintained a strong presence while negative sentiments such as safety concerns have emerged over time.