Feature selection techniques for effective model building and estimation on Twitter data to understand the political scenario in Latvia with supporting visualizations

In supervised learning, machine learning techniques can be applied to learn a model on

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.