Paper: Discovering Topical Aspects in Microblogs

ACL ID C14-1082
Title Discovering Topical Aspects in Microblogs
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

We address the problem of discovering topical phrases or ?aspects? from microblogging sites like Twitter, that correspond to key talking points or buzz around a particular topic or entity of interest. Inferring such topical aspects enables various applications such as trend detection and opinion mining for business analytics. However, mining high-volume microblog streams for aspects poses unique challenges due to the inherent noise, redundancy and ambiguity in users? social posts. We address these challenges by using a probabilistic model that incorporates various global and local indicators such as ?uniqueness?, ?diversity? and ?burstiness? of phrases, to infer relevant aspects. Our model is learned using an EM algorithm that uses automatically generated noisy labels, without requiring ma...