The government of Kenya has been experiencing low levels of public complaint feedback with regards to the current complaint reporting methods. Citizens consider the organized public meetings and office visits as time consuming and opt out of the complaint reporting process. However, citizens have been taken to complaining on social media to express their dissatisfaction with infrastructural service delivery by government. Recent efforts to improve low levels of interaction between governments and their citizens by mining data from social media have proven that citizen sourcing from social media is a suitable approach to solving this issue. Social media posts, comments, likes, favourites, shares, retweets and similar features are clear indicators of public feedback if the posts are related to public service. This study proposed the development of a tool that extracts and visualizes public complaints relating to Nairobi county from social media. Roads were the focus infrastructure category for the study. For classification of collected tweets, a Support Vector Classifier (SVC) with TF-IDF features was trained and tested using labelled tweets. The classifier was able to label tweets collected with an accuracy of 77.52%. Location information was retrieved from the tweets classified as complaints using the Stanford Core NLP named entity recognizer. The locations identified in the complaint tweets using the Stanford NER were reverse geocoded using the Google Geocoding API and the resulting geographic coordinates plotted on a static google map. Frequent words in the complaint tweets were represented using a WordCloud. The county government can use this information for decision making, planning and to give feedback to the public on resolution of the same