Predicting Personal Opinion on Future Events with Fingerprints (2020)

Abstract: Predicting users’ opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population’s opinion 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 an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one’s fingerprinting: 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. We introduce a recurrent neural network based model that integrates fingerprinting. 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.

## introduction opinion mining and sentiment analysis has important applications with practical socio-political and economical 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 @xcite @xcite @xcite @xcite @xcite . 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 @xcite . 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 the description of events, because people usually check breaking news about emerging events from news portals, micro-blogs, and social media @xcite and leave comments. we predict the opinion of users on all events regardless of whether they have read those articles. the key idea is to access the historical event-opinion pairs of users and learn about a user's fingerprinting, which we assume to be consistent over a period of time. we expect this fingerprinting ... event: hillary clinton reacts to controversial trump retweet: 'we need a real president'. comment: actually trump is still your president so don't cry. ... event: pelosi creates new house committee with subpoena power for coronavirus oversight. comment: still your president. btw trumps polls are higher than 2016 so that is a win. ... to implicitly capture what a person has said about events and assist opinion mining on unseen articles. for example, a user commented "still your president" for events about "hillary clinton" and "pelosi" as shown in ## method we describe the proposed model in this section. assuming that among k users we are interested in knowing the opinion of the user u k on a newly occurring event a t , and the user u k has previously left n article-comment pairs, i.e., [c t . an overview of the model is illustrated in figure 1 . since recurrent neural network architecture has achieved the state-of-the-art results on encoding sequences, we build our model using rnn for both words and the user history. specifically, we leverage two grus @xcite instead of lstm @xcite because the former has fewer parameters. the first gru, denoted as w-rnn in figure 1 for modeling words, encodes events and comments into fixed-length vectors, i.e, [h a,u k we describe the experimental setup in this section. we first explain how we process the dataset. then we detail the baselines and present the results. ## conclusion in this work, we introduce a new task that predicts opinion on unseen events based on one's reading and commenting history. we design a recurrent neural network based model to encode the historical data and use the fingerprint embedding to obtain opinion on new articles. experiments on our newly collected dataset show that leveraging recurrent neural network and one's historical data gives better performance that four used baselines. we believe that the proposed novel problem setting lays the foundation for a variety of more rigorous works to fully explore how to learn and generalize user fingerprints. in the future, we plan to quantify one's comment with more dimensions, such as emotion, and predict them on unseen events. we argue that a successful model would effectively leverage one's fingerprinting, and it is worthwhile to investigate different architectures for this task.