Extracting and understanding user sentiments for big data analytics in big business brands

Abstract

Consumer behavior has become the niche of the market for every user from a manufacturer to a customer. People are fairly good at expressing what they want, what they like, or even how much they will pay for an item. But they are not very good at accessing where that value comes from. Behavior is triggered from sentiments generated in response to an external stimulus. Sentiments and emotions are the subjects of study of sentiment analysis and opinion mining, and this field of study coincides with rapid growth of social media on the web, e.g. social networks, blogs and Twitter, and for the first time, we have huge volume (big data) of data in digital form with us to analyze. Developing algorithms for computers to recognize emotional expression is a widely studied area, and the study of big data analytics and neuromarketing techniques acts as the most powerful tool to develop these algorithms for better understanding of consumer preferences, purchase behavior and decision patterns. The research aims to extract/read user behavior/sentiment to predict future preferences and to plan the business branding policies. The major objective of this chapter is to perform data analytics of the sample data using Hadoop framework based on crucial metrics related to consumer behavior: (1) customer acquisition cost; (2) customer retention cost; (3) lifetime value; (4) customer satisfaction and happiness; and (5) average purchase amount and behavior. The understanding of these metrics helps in extraction of customer buying trends leading to match the specific customer personas, hence meeting business strategies. The chapter provides a study of user sentiment using neuromarketing techniques and providing data analytics on the user-recorded sentiments based on consumer behavior metrics. The chapter provides an understanding of (1) user sentiments, (2) consumer behavior and neuromarketing process and (3) big data analytics.