Title: An analysis of the 2016 US presidential election using Chanakya - a knowledge discovery platform for text mining
Authors: Rashmi Malhotra; Kunal Malhotra
Addresses: 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.
International Journal of Knowledge Engineering and Data Mining, 2018 Vol.5 No.1/2, pp.17 - 39
Available online: 19 Jun 2018Full-text access for editors Access for subscribers Free access Comment on this article