"Interactive text analytics for user-generated content" by Raheleh Makki with Prateek Jain as coordinator
Raheleh Makki · ACM SIGWEB Newsletter · 2017
The rapid growth of social media platforms, weblogs and online forums has made the volume of user-generated content increase exponentially in recent years. User-generated content is different from traditional documents in structure, length, and semantics. Consequently, applying traditional natural language processing and text mining methods to emerging and challenging text mining problems does not always achieve satisfactory results. In other words, as data changes, their characteristics and features change, and therefore the solutions that rely on certain assumptions about the data, which may no longer be valid, fail to perform as expected. In addition, the users' information needs may change over time, and hence are the type of applications that provide answers to these needs.