Detecting sentiment change in twitter streaming data

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Title:	Detecting sentiment change in twitter streaming data
Author:	Bifet Figuerol, Albert Carles; Holmes, Geoffrey; Pfahringer, Bernhard; Gavaldà Mestre, Ricard
Other authors:	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
Abstract:	MOA-TweetReader is a real-time system to read tweets in real time, to detect changes, and to fi nd 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.
Subject(s):	-Data stream mining -Massive online analysis -MOA -Mineria de dades -Microblogging
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Document type:	Article - Published version Conference Object
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