PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Detecting Sentiment Change in Twitter Streaming Data
Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Ricard Gavaldà
In: WAPA 2011, October 19-21, 2011, Castro Urdiales, Spain.


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. MOA-TweetReader is released under the GNU GPL license.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8650
Deposited By:Albert Bifet
Deposited On:17 February 2012