Mobile network failures have occurred many times in recent years. Some network failures become “silent” failures that mobile carriers cannot detect because of incomplete rules concerning failure detection by the network operating system. However, the increasing number of services and devices, and the increasing complexity of the network make it hard to generate rules that cover all network failures. Therefore, monitoring the network performance from a subscriber's perspective is very important. The traditional way to obtain feedback from subscribers is to use call centers and email. However, it is difficult to detect problems early or in their entirety through those channels because subscribers typically do not call a call center until they are certain the problem was caused by a network. In this paper, we discuss a way to monitor a social networking service (SNS) (Twitter in particular) to find out about problems that affect subscribers. A previous study showed the possibility of early detection of network problems by monitoring Twitter. However, since Twitter includes many conversation topics, it is difficult to find tweets that relate to network problems. Searching by a particular keyword is insufficient since it produces a lot of false positive results that contain the keyword but not the topic of the network problem. We solved this problem by using machine learning to suppress the false positive results. We implemented and evaluated a system to detect network failures from Twitter. As a result, we were able to identify 6 out of 6 large network problems and to suppress the number of false positives to only 6 events, whereas keyword matching detected 94 false positive events. Some of the problems were detected faster than through a call center. Furthermore, we conducted research in order to determine the appropriate machine learning algorithm, parameters, and volume of training data. We also propose a method to estimate the location where the tweeters were locate...