Open Source Social Media Intelligence (OSSMInt) is a field that focuses on extracting useful information and actionable insights from publicly available and overt sources of data on social media platforms. There are several applications that can be built by applying OSSMInt techniques on this human-sensor data. In this paper, we present some of the use-cases of OSSMInt that are useful for the public sector agencies for e-governance. E-governance on social media include the identification of complaints and grievances reported online by the public citizens for the government authorities and facilitate public agencies to response those complaints, provide better services and improve their connections with public citizens. We present the basic Natural Language Processing and Machine Learning based framework, tools and techniques within the context of OSSMInt and E-governance. The focus of this paper is on mining user-generated content on Twitter (the most popular social media and microblogging website) to identify public citizens complaints and grievances. In particular, we focus on two important applications: (1) complaints which are reported to spread awareness among other citizens and to bring government’s attention to the issues reported in the complaint, and (2) complaints which seek for immediate action and response from the concerned authorities. In addition to the basic introduction and motivation, we will discuss the unique challenges to these applications, open research problems, important literature, proposed approach, experimental results, and future directions.