  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.
