An analysis of the 2016 US presidential election using Chanakya - a knowledge discovery platform for text mining Online publication date: Fri, 29-Jun-2018
by Rashmi Malhotra; Kunal Malhotra
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 5, No. 1/2, 2018
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.
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