Recently, Twitter has been used as a citizen-engaging platform by Indian Railway Ministry (IRM) for collecting civic issues. However, due to the manual inspection model, a large percentage of complaints are left unaddressed affecting the credibility of the citizen-sourcing mediums. The existing solutions are for English reports and unable to capture the diverse range of code-mixed languages used in the complaints. In this paper, we developed a multilingual cased version of BERT (mBERT) for automated identification of monolingual and multilingual (aka code- mixed) complaints. The proposed mBERT is a transformers- based model pre-trained on multilingual Wikipedia corpus. In addition to the grievances classification, we also employ a BERT multi-label classifier for labelling the tweets with the issues reported in the complaints. The proposed solution approach obtains an accuracy of 82% with the binary classifier and overall accuracy of 95% with the multi-label model. Additionally, a critical analysis is presented on the classified reports and a location-based analysis to visualise the velocity and veracity of complaints across India.