Marjan Hosseinia


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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.


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Stance Prediction for Contemporary Issues: Data and Experiments
Marjan Hosseinia | Eduard Dragut | Arjun Mukherjee
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.