  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.

Computation and Language;Social and Information Networks