Dainis Boumber


On the Usefulness of Personality Traits in Opinion-oriented Tasks
Marjan Hosseinia | Eduard Dragut | Dainis Boumber | Arjun Mukherjee
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.


Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution
Dainis Boumber | Yifan Zhang | Arjun Mukherjee
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)