This work focuses on the area of Smart Governance in Smart Cities, which entails transparency in government through public involvement. Specifically, it describes our research on mining urban ordinances or local laws and the public reactions to them expressed on the social media site Twitter. We mine ordinances and tweets related to each other through their mutual connection with Smart City Characteristics (SCCs) and conduct sentiment analysis of relevant tweets for analyzing opinions of the public on local laws in the given urban region. This helps assess how well that region heads towards a Smart City based on (1) how closely ordinances map to the respective SCCs and (2) the extent of public satisfaction on ordinances related to those SCCs. The mining process relies on Commonsense Knowledge (CSK), i.e., knowledge that is obvious to humans but needs to be explicitly fed into machines for automation. CSK is useful in filtering during tweet selection, conducting SCC-based ordinancetweet mapping and performing sentiment analysis of tweets. This paper presents our work in mapping ordinances to tweets through single or multiple SCCs and opinion mining of tweets along with an experimental evaluation and a discussion with useful recommendations.