Scaling archived social media data analysis using a hadoop cloud

Conejero, Javier, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 and Morgan, Jeffrey 2013. Scaling archived social media data analysis using a hadoop cloud. Presented at: IEEE 6th International Conference on Cloud Computing (CLOUD), Santa Clara, CA, USA, 27 June - 2 July 2013. IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE,

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Abstract

Over recent years, there has been an emerging interest in supporting social media analysis for marketing, opin- ion analysis and understanding community cohesion. Social media data conforms to many of the categorisations attributed to “big-data” – i.e. volume, velocity and variety. Generally analysis needs to be undertaken over large volumes of data in an efficient and timely manner. A variety of computational infrastructures have been reported to achieve this. We present the COSMOS platform supporting sentiment and tension analysis on Twitter data, and demonstrate how this platform can be scaled using the OpenNebula Cloud environment with Map/Reduce-based analysis using Hadoop. In particular, we describe the types of system configurations that would be most useful from a performance perspective – i.e. how virtual machines in the infrastructure should be distributed to reduce variability in the analysis performance. We demonstrate the approach using a data set consisting of several million Twitter messages, analysed over two types of Cloud infrastructure.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
Funders:	ESRC
Related URLs:	Organisation
Date of First Compliant Deposit:	30 March 2016
Last Modified:	10 Nov 2022 13:05
URI:	https://orca.cardiff.ac.uk/id/eprint/47387

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