“Opinion Prediction with User Fingerprinting” by Kishore Tumarada, Yifan Zhang, Fan Yang, Eduard Dragut, Omprakash Gnawali, and Arjun Mukherjee. In Recent Advances in Natural Language Processing (RANLP 2021), Sep. 2021.
Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user’s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user’s comments conditioned on relevant user’s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13% improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
BibTeX entry:
@inproceedings{opinion-ranlp21,
author = {Kishore Tumarada and Yifan Zhang and Fan Yang and Eduard
Dragut and Omprakash Gnawali and Arjun Mukherjee},
title = {Opinion Prediction with User Fingerprinting},
booktitle = {Recent Advances in Natural Language Processing (RANLP 2021)},
month = sep,
year = {2021}
}