Value-based prediction of election results using natural language processing: A case of the New Zealand General Election

Abstract

In this article, we report the results of a study that tested a values-based method of predicting political election results. The study was carried out on the 2014 New Zealand General Election, randomly selecting a stratified sample from a consumer panel. The survey of 858 participants used open-ended questions to invoke and capture values relevant to the election. By using corpus linguistic analysis techniques, terms were ranked by weighting based on a log-frequency entropy method. Lexicons for Lasswell and Kaplan’s societal value framework reduced the corpus of term-weighted documents to a workable number of eight user-defined societal value-topics. The topics were regressed onto the individual voting decision using a multinomial logit (MNL) regression. The mean absolute deviation (MAD) from the actual vote was 1.8%, much less than the margin of error of 3.5% expected from sampling error alone. The methodology was successful in predicting the outcome for the minor parties with good accuracy, for example, the prediction for the then newly formed Internet-Mana was out by about 0.5%. The framing-balanced, value-based predictions exhibited reasonable stability, considering they were made six weeks before Election Day. Thus the values relevant to the voters and a good prediction of the voting behavior became evident ahead of the official campaign period, which started four weeks before Election Day in New Zealand. Our study concluded that the value-based prediction shows promise for improving the quality of political journalism and public engagement in the period of election campaigns, and will assist greatly in focusing public debate more on values that are influential on citizens’ voting decisions.