Can We Predict Political Poll Results by Using Blog Entries?
Can We Predict Political Poll Results by Using Blog Entries?
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我们可以使用博客条目预测政治民意调查结果吗？
DOI：
10.1109/hicss.2012.145
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Takahisa Suzuki
中科院分区：
文献类型：
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作者：
Manabu Okumura;Tetsuya Motegi;Tetsuro Kobayashi;Keizo Oyama;Takahisa Suzuki
博客已经成为人们在网上发表意见和想法的重要媒介。但是，能否从博客中分析政治舆论，目前还不清楚。最近有一些关于政治观点分类的工作，但大多数只将政治博客条目或网站分类为对立观点，如保守/自由或以色列/巴勒斯坦。然而，为了预测更广泛的政治观点，我们需要分析各种各样的博客。因此，我们构建了一个与政治投票结果相关的一般博客的数据集。有了这个数据集，我们进行了实验，通过使用博客条目来预测政治投票结果。我们的预测方法是基于一种监督学习算法，支持向量机（SVM），具有博客网站的特征。我们还尝试用三个人类受试者作为系统性能的上界进行人工预测，发现即使对人类来说，这样的任务也相当困难，系统的性能可以超过人类。
Blogs have become an important medium for people to publish their opinions and ideas on the Web. However, it is still not clear whether we can analyze political public opinions from blogs. There have been some recent work on political viewpoint classification, but most only classified political blog entries or sites into opposing viewpoints such as conservative/liberal or Israeli/Palestinian. However, to predict a broader range of political opinions, we need to analyze a wide variety of blogs. Therefore, we constructed a dataset of general blogs that are connected to political poll results. With the dataset, we conducted experiments to predict political poll results by using the blog entries. Our prediction methods are based on a supervised learning algorithm, Support Vector Machines (SVM), with features in blog sites. We also attempted manual prediction with three human subjects as the upper bound of the system performance, and found that such a task is rather difficult even for humans and that the system performance can outperform that of humans.