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Trendminer: an architecture for real time analysis of social media text

Preotiuc-Pietro, Daniel, Samangooei, Sina, Cohn, Trevor, Gibbins, Nicholas and Niranjan, Mahesan (2012) Trendminer: an architecture for real time analysis of social media text At 6th International AAAI Conference on Weblogs and Social Media (ICWSM-12), Ireland. 05 - 07 Jun 2012. 5 pp.

Record type: Conference or Workshop Item (Paper)


The emergence of online social networks (OSNs) and the accompanying availability of large amounts of data, pose a number of new natural language processing (NLP) and computational challenges. Data from OSNs is different to data from traditional sources (e.g. newswire). The texts are short, noisy and conversational. Another important issue is that data occurs in a real-time streams, needing immediate analysis that is grounded in time and context. In this paper we describe a new open-source framework for efficient text processing of streaming OSN data (available at Whilst researchers have made progress in adapting or creating text analysis tools for OSN data, a system to unify these tasks has yet to be built. Our system is focused on a real world scenario where fast processing and accuracy is paramount. We use the MapReduce framework for distributed computing and present running times for our system in order to show that scaling to online scenarios is feasible.We describe the components of the system and evaluate their accuracy. Our system supports easy integration of future modules in order to extend its functionality.

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e-pub ahead of print date: June 2012
Published date: June 2012
Venue - Dates: 6th International AAAI Conference on Weblogs and Social Media (ICWSM-12), Ireland, 2012-06-05 - 2012-06-07
Organisations: Web & Internet Science


Local EPrints ID: 340056
PURE UUID: 260b480b-cf54-4e2f-9a6a-cc803c0afc19
ORCID for Nicholas Gibbins: ORCID iD

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Date deposited: 08 Jun 2012 08:44
Last modified: 18 Jul 2017 05:50

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Author: Daniel Preotiuc-Pietro
Author: Sina Samangooei
Author: Trevor Cohn
Author: Nicholas Gibbins ORCID iD

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