AN ANALYSIS OF THE 2016 UNITED STATES PRESIDENTIAL ELECTION USING CHANAKYA - A KNOWLEDGE DISCOVERY PLATFORM FOR TEXT MINING
Rashmi Malhotra, Kunal Malhotra · International Journal of Knowledge Engineering and Data Mining · 2018
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.