ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Microblogs--Social aspects.
- Creators: Davulcu, Hasan
a small set of labeled documents which can be used to classify a larger set of unknown
documents. Machine learning techniques can be used to analyze a political scenario
in a given society. A lot of research has been going on in this field to understand
the interactions of various people in the society in response to actions taken by their
organizations.
This paper talks about understanding the Russian influence on people in Latvia.
This is done by building an eeffective model learnt on initial set of documents
containing a combination of official party web-pages, important political leaders' social
networking sites. Since twitter is a micro-blogging site which allows people to post
their opinions on any topic, the model built is used for estimating the tweets sup-
porting the Russian and Latvian political organizations in Latvia. All the documents
collected for analysis are in Latvian and Russian languages which are rich in vocabulary resulting into huge number of features. Hence, feature selection techniques can
be used to reduce the vocabulary set relevant to the classification model. This thesis
provides a comparative analysis of traditional feature selection techniques and implementation of a new iterative feature selection method using EM and cross-domain
training along with supportive visualization tool. This method out performed other
feature selection methods by reducing the number of features up-to 50% along with
good model accuracy. The results from the classification are used to interpret user
behavior and their political influence patterns across organizations in Latvia using
interactive dashboard with combination of powerful widgets.