Name
Abstract | ||
|---|---|---|
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 useru0027s 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 useru0027s industry with 64.3% accuracy, significantly outperforming the majority baseline in a taxonomy of fourteen industry classes. Our qualitative analysis suggests that a personu0027s industry not only affects the words used and their perceived meanings, but also the number and type of emotions being expressed. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Social media,Multitude,Profiling (computer programming),Computer science,Feature engineering,Artificial intelligence,Ensemble learning,Machine learning |
DocType | Volume | Citations |
Journal | abs/1612.08205 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Pappas | 1 | 0 | 1.01 |
| Rada Mihalcea | 2 | 6460 | 445.54 |