Dr. Michel Ballings joined the Department of Business Analytics and Statistics in 2014 after earning his Ph.D. in Applied Economics from Ghent University (Belgium). He holds a master’s degree in Business Economics from VLEKHO Business School in Brussels and a bachelor’s degree in Business Administration from University College Leuven.
He teaches Marketing Analytics focusing on predictive analytics for analytical Customer Relationship Management (aCRM), including customer acquisition, customer retention, and customer development. His class gears up statistics, programming and functional marketing knowledge aimed at equipping graduates to distill data into valuable insights.
His current research interests are concentrated in Social Media Analytics (SMA), a multi-disciplinary, data-intensive, and collaborative research area that lies at the intersection of computing, social media management, and machine learning.
Dr. Ballings has worked with organizations such as Anheuser Bush InBev, Carglass, Vodafone, Friesland Campina, USG People, Concentra, Essent, and Club Brugge. He has research articles published in refereed journals and conference proceedings, such as Expert Systems With Applications, IEEE International Conference on Data Mining Workshops, Studies in Classification – Data Analysis and Knowledge Organization, and Management Intelligent Systems.
The added value of auxiliary data in sentiment analysis of Facebook posts
The purpose of this study is to (1) assess the added value of information available before (i.e., leading) and after (i.e., lagging) the focal post’s creation time in sentiment analysis of Facebook posts, (2) determine which predictors are most important, and (3) investigate the relationship between top predictors and sentiment. We build a sentiment prediction model, including leading information, lagging information, and traditional post variables. We benchmark Random Forest and Support Vector Machines using five times twofold cross-validation. The results indicate that both leading and lagging information increase the model’s predictive performance. The most important predictors include the number of uppercase letters, the number of likes and the number of negative comments. A higher number of uppercase letters and likes increases the likelihood of a positive post, while a higher number of comments increases the likelihood of a negative post. The main contribution of this study is that it is the first to assess the added value of leading and lagging information in the context of sentiment analysis.