Title: An analysis of the 2016 US presidential election using Chanakya - a knowledge discovery platform for text mining
Authors: Rashmi Malhotra; Kunal Malhotra
Decision and Systems Sciences Department, Saint Joseph's University, 5600 City Avenue, Philadelphia, PA 19131, USA
Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, 3330 Walnut Street, Levine Hall, Philadelphia, PA 19104-6309, USA
Abstract: In this era of information overload, discovering knowledge is a challenge. However, a new generation of text mining tools enables researchers and practitioners to analyse large volumes of data. This paper illustrates the design of knowledge discovery system - Chanakya using text mining. Chanakya works in two stages. Stage 1 uses naive Bayes classifier, a supervised machine-learning algorithm to train for classes, as we explicitly provide training data that is labelled with classes. Stage 2 uses k-means analysis, an unsupervised machine-learning algorithm to determine what categories are emerging from the mentions of each class. We use the 2016 presidential elections Twitter feeds to illustrate the use of Chanakya. Chanakya offers a commentary on the current state of the political arena after analysing the candidate tweets and how people are reacting to these tweets.
Keywords: text mining; k-means analysis; supervised machine learning; Bayes classifier; knowledge discovery; USA.
Int. J. of Knowledge Engineering and Data Mining, 2018 Vol.5, No.1/2, pp.17 - 39
Available online: 19 Jun 2018