Government 2.0 activities have become very attractive and popular. Using the platforms to support the activities, anyone can anytime report issues in a city on the Web and share the reports with other people. Since a variety of reports are posted, officials in the city management section have to give priorities to the reports. However, it is not easy task to judge the importance of the reports since importance judgments vary depending on the officials and consequently the agreement rate becomes low. To remedy the low agreement rate problem of human judgment, it is necessary to create an automatic method to find reports with high priorities. Hirokawa et al. employed the Support Vector Machine (SVM) with word feature selection method (SVM+FS) to detect signs of danger from posted reports because signs of danger is one of high priority issues to be dealt with. However they did not compare the SVM+FS method with other conventional machine learning methods and it is not clear whether or not the SVM+FS method has better performance than the other methods. This paper compared the results of the SVM+FS method with conventional machine learning methods: SVM, Random Forest, and Naïve Bayse with conventional word vectors, an LDA-based document vector, and word embedding by Word2Vec. Experimental results illustrate the validity and effectiveness of the SVM+FS method.