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Topic Analysis of Tweets on the European Refugee Crisis Using Non-negative Matrix Factorization

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.

Identiferoai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:cmc_theses-2412
Date01 January 2016
CreatorsShen, Chong
PublisherScholarship @ Claremont
Source SetsClaremont Colleges
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceCMC Senior Theses
Rights© 2016 Chong Shen, default

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