Predicting Personal Opinion on Future Events with Fingerprints

Proceedings of the 28th International Conference on Computational Linguistics, pages 1802–1807 Barcelona, Spain (Online), December 8-13, 2020 1802 Predicting Personal Opinion on Future Events with Fingerprints Fan Yang∗ University of Houston Houston, USA fyangskip@gmail.com Eduard Dragut Temple University Philadelphia, USA edragut@temple.edu Arjun Mukherjee University of Houston Houston, USA arjun@cs.uh.edu Abstract Predicting users’ opinions in their response to social events has important real-world applications, many of which with political and social impacts. Existing approaches derive a population’s opin- ion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on an user’s fin- gerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user’s future opinion to an unseen event or topic. We introduce a recurrent neural network based model that integrates fingerprint- ing. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach. 1 Introduction Opinion mining and sentiment analysis has important applications with practical socio-political and eco- nomical benefits. There are numerous examples of works that show applications of sentiment analysis beyond classification. It can be used for analyzing political preferences of the electorate or for mining sentiments and emotions of people who lived in the past. The goal of these studies is not only to recognize sentiments, but also to understand how they were formed. All these approaches share the same starting point: abundant availability of user-generated content (UGC), such as reviews and Twitter posts (Dave et al., 2003; Hu and Liu, 2004; Pang and Lee, 2008; Pak and Paroubek, 2010; Liu, 2010; Taboada et al., 2011; Liu, 2012; Zhu et al., 2011; Yang et al., 2016; Joulin et al., 2017). They focus on document classification and predict sentiment, stance, subjectivity, or aspect. The success of these methods relies on the assumption that people have already expressed their opinion on a topic or event. However, we may not acquire those opinion if people are not willing to reveal them or did not have the chance to express their opinions (e.g., about a new piece of legislature). Another instance is the availability of biased content on the web. One cannot possibly read all the content on the web and can only peruse a tiny part of the information, and people often read the content that is consistent with their prior beliefs (Mullainathan and Shleifer, 2005; Xiang and Sarvary, 2007). As a result, existing methods cannot deal with the situation when there is no user-generated content and might fail to collect opinion on certain topics. We aim to fill this gap. We propose to predict opinion about public events before people leave their comments. We consider news articles as …

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