The Twitter platform has emerged as a leading medium of conducting social commentary, where users remark upon all kinds of entities, events and occurrences. As a result, organizations are starting to mine twitter posts to unearth the knowledge encoded in such commentary. Mobile applications, commonly known as mobile apps, are the fastest growing consumer product segment in the history of human merchandizing, with over 600,000 apps on the Apple platform and over 350,000 on Android. A particularly interesting issue is to evaluate the popularity of specific mobile apps by analyzing the social conversation on them. Clearly, twitter posts related to apps are an important segment of this conversation and have been a main area of research for us. In this respect, one particularly important problem arises due to a name conflict of mobile app names and the names that are used to refer the mobile apps in twitter posts. In this paper, we present a strategy to reliably extract twitter posts that are related to specific apps, but discovering the contextual clues that enable effective filtering of irrelevant twitter posts is our concern. While our application is in the important space of mobile apps, our techniques are completely general and may be applied to any entity class. We have evaluated our approach against a popular Bayesian classifier and a commercial solution. We have demonstrated that our approach is significantly more accurate than both of these. These results as well as other theoretical and practical implications are discussed.