A prototype model for deriving social media intelligence using opinion mining from microblog data

Abstract

Online conversation has become very popular in recent years. Many people share their experience and opinions of an interesting topic on the social networking sites especially on microblogs. Since the opinions are preserved like historical data in the social networking sites, an individual’s opinion has got the power to change trends and strategies of an organization. Organizations use those social media data to interpret and extract knowledge in their strategic decision-making process referred as social media intelligence. This article provides an efficient approach to extract opinions using text mining techniques integrated with supervised learning methods, classified and rated against an evaluation criteria ranging from excellent, good, average, and poor using fuzzy logic.