Predicting the Attitudes of Opinion Holders (Leaders)

The purpose of this study is to provide a solution to predict the stance of opinion holders regarding certain issue statements that they already expressed in publicly available sources.In the political environment, experts forecast parties' positions on certain policy issues. This process is also known as 'coding'. The resulting forecasts of the codings are needed for web-based Voting Advice Applications (VAAs), where users can compare their political position with that of the different parties. Conducting these forecasts is expensive with regards to the experts required and time spent.This problem was investigated in this study by applying a design research methodology. In a rst phase, knowledge was gained about VAAs and the forecasting process of the political experts. On that basis, a prototype of a prediction software was built. This software was tested against real-world coding results from the United Kingdom parliamentary elections of 2015. The software is able to predict the stance of political parties with an accuracy of 52%. Besides its prediction accuracy, the software generates comprehensive reports with prediction details that contain valuable information for coders(such as justications for a party's stance). The prediction accuracy is rather low and will not provide reliable forecasts. On the other hand, the reports offer according to a coding expert advantages in terms of process improvements for coders. However, this statement could not be proved quantitatively. Through further analysis, several opportunities for future research were identified.