Predicting Personal Opinion on Future Events with Fingerprints
Predicting Personal Opinion on Future Events with Fingerprints
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DOI：
10.18653/v1/2020.coling-main.162
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Fan Yang;Eduard Constantin Dragut;Arjun Mukherjee
中科院分区：
文献类型：
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作者：
Fan Yang;Eduard Constantin Dragut;Arjun Mukherjee
预测用户对社交事件的反应具有重要的现实应用，其中许多具有政治和社会影响。现有的方法从大量的用户生成的内容中得出人群对正在进行的事件的意见。在某些情况下，我们可能无法获得这些内容，因此无法对这些新出现的事件推断出公正的意见。为了解决这个问题，我们建议探索意见看不见的文章的基础上一个人的指纹：以前的阅读和评论的历史。这项工作提出了一个重点研究建模和利用指纹技术来预测用户的未来意见。我们介绍了一个基于递归神经网络的模型，集成了指纹。我们收集了一个大型的数据集，包括事件评论对从六个新闻网站。我们在这个数据集上评估了所提出的模型。结果表明，我们的方法的有效性表现出显着的性能增益。
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.