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Text Mining in Twitter with Spark and Scala

Adam, Simitos

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.

masterThesis

Information retrieval--Data processing
Information retrieval--Technological innovations
SPARK (Computer program language)
Data mining--Social aspects
Social media
Twitter
Information retrieval
Spark (Electronic resource : Apache Software Foundation)
Data mining
Data mining--Computer programs
Twitter--Political aspects--Greece
Data mining--Data processing
Data mining--Statistical methods
Twitter--Social aspects
Social media--Political aspects
Scala (Computer program language)
Information Technology


Αγγλική γλώσσα

2017-05-10T00:00:17Z
2017-05-09T07:15:24Z
2017-05-09


IHU
ihu




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