Research shows that Twitter is being used as a platform to not only share and disseminate the information but also collecting complaints from citizens. However, due to the presence of high volume and large stream data, real-time manual identification of those complaints is overwhelmingly impractical. In this paper, we identify the complaints and grievances posted on bad road conditions causing life risks, discomfort and poor road experience to the citizens. We formulate the problem of killer road complaints identification as a multiclass text classification problem. We address the challenge of keyword based flagging methods and identify several linguistic features that are unique for the killer road complaints tweets such as the issue reported in the complaint, pinpoint location of the issue, city or region location information. Our results reveal that not all complaint reports posted to Public agencies contain the sufficient information and are not useful. Therefore, we further propose a mechanism to enrich the nearly-useful tweets and convert them into useful reports. We present our results using information visualization and gain actionable insights from them. Our results show that the proposed features are discriminatory and able to classify killer roads complaints with an accuracy of 67% and a recall of 65%.