Trend sensing via Twitter

Due to its ever increasing popularity, Twitter has become a pervasive information outlet. In this paper, we present a passive sensing framework for identifying trends via Twitter. In our framework, we use a multi–dimensional corpus for fine–granularity sensing of trends, and employ both vector–space and set–space methods for achieving accuracy. We present two applications of our framework. The first one is sensing trends in public opinion by using an emotion–category corpus. The second application is sensing trends in location–types in a city by using a location–category corpus. Our experiments show that the proposed methods are able to determine changes in trends effectively in both application scenarios.