Social media have substantially altered the way brands and businesses advertise: Online Social Networks provide brands with more versatile and dynamic channels for advertisement than traditional media (e.g., TV and radio). Levels of engagement in such media are usually measured in terms of content adoption (e.g., likes and retweets) and sentiment, around a given topic. However, sentiment analysis and topic identification are both non-trivial tasks. In this paper, using data collected from Twitter as a case study, we analyze how engagement and sentiment in promoted content spread over a 10-day period. We find that promoted tweets lead to higher positive sentiment than promoted trends; although promoted trends pay off in response volume. We observe that levels of engagement for the brand and promoted content are highest on the first day of the campaign, and fall considerably thereafter. However, we show that these insights depend on the use of robust machine learning and natural language processing techniques to gather focused, relevant datasets, and to accurately gauge sentiment, rather than relying on the simple keyword- or frequency-based metrics sometimes used in social media research.