Opinion Prediction with User Fingerprinting

Kishore Tumarada, Yifan Zhang, Fan Yang, Eduard Dragut, Omprakash Gnawali, Arjun Mukherjee


Abstract
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.
Anthology ID:
2021.ranlp-1.159
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1423–1431
Language:
URL:
https://aclanthology.org/2021.ranlp-1.159
DOI:
Bibkey:
Cite (ACL):
Kishore Tumarada, Yifan Zhang, Fan Yang, Eduard Dragut, Omprakash Gnawali, and Arjun Mukherjee. 2021. Opinion Prediction with User Fingerprinting. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1423–1431, Held Online. INCOMA Ltd..
Cite (Informal):
Opinion Prediction with User Fingerprinting (Tumarada et al., RANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.ranlp-1.159.pdf
Code
 kishoret04/opinionprediction