In this paper, we present computational models to predict Twitter users' attitude towards a specific brand through their personal and social characteristics. We also predict their likelihood to take different actions based on their attitudes. In order to operationalize our research on users' attitude and actions, we collected ground-truth data through surveys of Twitter users. We have conducted experiments using two real world datasets to validate the effectiveness of our attitude and action prediction framework. Finally, we show how our models can be integrated with a visual analytics system for customer intervention.
@article{arxiv.1704.04723,
title = {Computational Models for Attitude and Actions Prediction},
author = {Jalal Mahmud and Geli Fei and Anbang Xu and Aditya Pal and Michelle Zhou},
journal= {arXiv preprint arXiv:1704.04723},
year = {2017}
}
Comments
This is an extended version of a previously published IUI 2016 paper from same authors. http://dl.acm.org/citation.cfm?id=2856800