Automatic profiling of social media users is an important task for supporting a multitude of downstream applications. While a number of studies have used social media content to extract and study collective social attributes, there is a lack of substantial research that addresses the detection of a user's industry. We frame this task as classification using both feature engineering and ensemble learning. Our industry-detection system uses both posted content and profile information to detect a user's industry with 64.3% accuracy, significantly outperforming the majority baseline in a taxonomy of fourteen industry classes. Our qualitative analysis suggests that a person's industry not only affects the words used and their perceived meanings, but also the number and type of emotions being expressed.
@article{arxiv.1612.08205,
title = {Predicting the Industry of Users on Social Media},
author = {Konstantinos Pappas and Rada Mihalcea},
journal= {arXiv preprint arXiv:1612.08205},
year = {2016}
}