Predictive Analytics Using Social Big Data and Machine Learning

Bilal Abu-Salih (), Pornpit Wongthongtham (), Dengya Zhu (), Kit Yan Chan () and Amit Rudra ()
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Bilal Abu-Salih: The University of Jordan
Pornpit Wongthongtham: The University of Western Australia
Dengya Zhu: Curtin University
Kit Yan Chan: Curtin University
Amit Rudra: Curtin University

Chapter Chapter 5 in Social Big Data Analytics, 2021, pp 113-143 from Springer

Abstract: Abstract The ever-increase in the quality and quantity of data generated from day-to-day businesses operations in conjunction with the continuously imported related social data have made the traditional statistical approaches inadequate to tackle such data floods. This has dictated researchers to design and develop advance and sophisticated analytics that can be incorporated to gain valuable insights that benefit business domain. This chapter sheds the light on core aspects that lay the foundations for social big data analytics. In particular, the significant of predictive analytics in the context of SBD is discussed fortified with presenting a framework for SBD predictive analytics. Then, various predictive analytical algorithms are introduced with their usage in several important application and top-tier tools and APIs. A case study on using predictive analytics to social data is provided supports with experiments to substantiate significance and utility of predictive analytics.

Keywords: Predictive analytics; Machine learning; Social media classification; Predictive analytics tools and APIs; Social politics (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-981-33-6652-7_5

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