Text Mining in Twitter with Spark and Scala
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Ημερομηνία
2017-05-09
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This dissertation was written as a part of the MSc in “Mobile and Web Computing” at the
International Hellenic University, Thessaloniki, Greece. Text Mining is a research area
that tries to solve the document overabundance problem by using Data Mining, Machine
Learning, Natural Language Processing, Information Retrieval, and Knowledge Management
techniques.
Text
Mining’s
main
purpose
is
the
automate
documents
categorization
in
classes.
People’s thoughts and opinions have always been studied and researched by the
sciences of sociology and history. Social Media revolution has made opinion expression
a very easy, simple and quick procedure. Thanks to Social Media an Internet user can
propagate their opinion and read other users’ opinions as well. As a result, the Internet is
“flooded” by a vast volume of data that is difficult to be managed. Social Media is one of
the factors that contribute to the phenomenon called “Big Data” in computer science.
The object of this master thesis is the collection and manipulation of social media
users’ opinions about political situation in Greece by using text mining methods.
Specifically, the application developed crawls opinions for Greek parliament members
from Twitter social medium and categorizes them in positive, neutral, and negative.
Statistics produced are indicative for each member’s popularity.