Bifet, A., Holmes, G., Pfahringer, B., & Gavaldà, R. (2011). Detecting sentiment change in Twitter streaming data. In T. Diethe, J. L. Balcázar, J. Shawe-Taylor, & C. Tȋrnăucă (Eds.), Proceedings of 2nd Workshop on Applications of Pattern Analysis (pp. 5–11). Castro Urdiales, Spain: JMLR.
Abstract
MOA-TweetReader is a real-time system to read tweets in real time, to detect changes, and to find the terms whose frequency changed. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. MOA-TweetReader is a software extension to the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams.
Rights
© 2011 A. Bifet, G. Holmes, B. Pfahringer & R. Gavaldà.
