Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews

Abstract

In the current era of social media, different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events, campaigns, and elections. The acquisition, analysis, and presentation of such content have received considerable attention from opinion-mining researchers. For this purpose, different supervised and unsupervised techniques have been used. However, they have produced less efficient results, which need to be improved by incorporating additional classifiers with the extended data sets. The authors investigate different supervised machine learning classifiers for classifying the political affiliations of users. For this purpose, a data set of political reviews is acquired from Twitter and annotated with different polarity classes. After pre-processing, different machine learning classifiers like K-nearest neighbor, naïve Bayes, support vector machine, extreme gradient boosting, and others, are applied. Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations.