The North American Telecommunications sector is one of the leading mobile broadband sectors worldwide, representing increasingly important revenue opportunities for mobile operators. Taking into consideration that the market is being saturated and revenue from new subscriptions is increasingly deteriorating, mobile carriers tend to focus on customer service and high levels of customer satisfaction in order to retain customers and maintain a low churn rate. In this context, it is a matter of critical importance to be able to measure the overall customer satisfaction level, by explicitly or implicitly mining the public opinion towards this end. In this paper, we argue that online social media can be exploited as a proxy to infer customer satisfaction through the utilization of automated, machine-learning based sentiment analysis techniques. Our work focuses on the two leading mobile broadband carriers located in the broader North American area, AT&T and Verizon, by analysing tweets fetched during a 15-day period within February 2013, to assess relative customer satisfaction degrees. The validity of our approach is justified through comparison against surveys conducted during 2012 from Forrester and Vocalabs in terms of customer satisfaction on the overall brand - usage experience.