Analysing brand sentiment with social media and open source Big Data tools

Ankur Gupta and Kishore Jhunjhunwala

Journal of Digital & Social Media Marketing, 2016, vol. 3, issue 4, 338-347

Abstract: Branding is all about people, namely consumers. Through social media, marketers have a single platform that can provide great insights into consumer behaviour and sentiments about their brands. But leveraging this information to its fullest potential to understand how consumers think about products and brands can be cost-prohibitive or even impossible with traditional tools. Open source Big Data technologies can level the playing field for analysing customer sentiment and perception. Tools such as Hadoop and NoSQL not only make it possible to process and store large volumes of unstructured social media data cost-effectively, they provide marketers with powerful analytics capabilities for integrating new data sources with traditional enterprise data to achieve a 360° view of the customer. This paper walks the reader through modern data management and analytics frameworks that enterprises are using to gain new customer insights. It provides details on the technologies and methodologies used to enable advanced customer analytics with social media, as well as specific real-world use cases for real-time sentiment analysis and product perception analysis. Brand managers and technology leaders alike will gain an understanding of how open source Big Data frameworks can provide a cost-effective, flexible and scalable solution for leveraging social media data.

Keywords: sentiment analysis; perception analysis; social media; Big Data; Hadoop; open source (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2016
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