The rapid growth of the World Wide Web and social media allows users playing an active role in the contents’ creation process. Users can evaluate the brands’ reputation and quality exploiting the information provided by new marketing channels, such as social media, social networks, and electronic commerce (or e-commerce). Consequently, enterprises need to spot and analyze these digital data in order to improve their reputation among the consumers. The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing, providing the readers with a deep understanding of the state of the art solutions.

Benedetto, F., Tedeschi, A. (2016). Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In Studies in Computational Intelligence (pp. 341-377). Springer Verlag [10.1007/978-3-319-30319-2_14].

Big data sentiment analysis for brand monitoring in social media streams by cloud computing

BENEDETTO, FRANCESCO;TEDESCHI, ANTONIO
2016-01-01

Abstract

The rapid growth of the World Wide Web and social media allows users playing an active role in the contents’ creation process. Users can evaluate the brands’ reputation and quality exploiting the information provided by new marketing channels, such as social media, social networks, and electronic commerce (or e-commerce). Consequently, enterprises need to spot and analyze these digital data in order to improve their reputation among the consumers. The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing, providing the readers with a deep understanding of the state of the art solutions.
2016
978-3-319-30317-8
978-3-319-30319-2
978-3-319-30317-8
978-3-319-30319-2
Benedetto, F., Tedeschi, A. (2016). Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In Studies in Computational Intelligence (pp. 341-377). Springer Verlag [10.1007/978-3-319-30319-2_14].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/300539
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