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.