Knowledge discovery from social media using big data-provided sentiment analysis (SoMABiT). (December 2015)

• Record Type: Journal Article
• Authors: Bohlouli, Mahdi; Dalter, Jens; Dornhöfer, Mareike; Zenkert, Johannes; Fathi, Madjid
• Is Part Of: Journal of information science. Volume 41:Number 6(2015)
• Page Start: 779
• Page End: 798
• Publication Date: 2015-12
• Subjects: Big data analytics -- knowledge discovery -- NoSQL databases -- product use information -- semantic technologies -- sentiment analysis -- social media analysis; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551515602846
• Languages: English
• ISSNs: 0165-5515

Abstract

In today's competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries. To this aim, the use of efficient analytical algorithms ensures better understanding of customer feedback and improves the next generation of products. Accordingly, the dramatic increase in the use of social media in daily life provides beneficial sources for market analytics. Yet how traditional analytic algorithms and methods can be scaled up for such disparate and multistructured data sources is a major challenge. This paper presents and discusses the technological and scientific focus of SoMABiT as a social media analysis platform using big data technology. Sentiment analysis has been employed in order to discover knowledge from social media. The use of MapReduce and the development of a distributed algorithm towards an integrated platform that can scale for any data volume and provide social media-driven knowledge is the main novelty of the proposed concept in comparison to the state-of-the-art technologies.