Social media sentiment analysis for brand monitoring
Social media (SM) has had a profound effect on the business ecosystem. It provides immense potential for businesses because consumers habitually log on to it daily and are exposed to companies. Companies depend on sentiment analysis (SA) to gain a deeper understanding of the consumer mindset. SA can be of assistance so that you gain insights about new markets, foresee industry trends, and most importantly, understand what didn't go well with previous product releases to help you improve your existing and future products and services. Obtaining an accurate and truly useful information from SM sentiment analysis has become a challenging issue in recent years. In this paper, we propose a web-based brand monitoring system that helps businesses and organizations monitor public sentiments or opinions about their brand, product or services. SM platform Twitter was used as a case study, involving the 2016 United States presidential election twitter dataset. The study utilized statistics, natural language processing, and machine learning to determine the emotional meaning of communications. The developed system facilitates informed decisions and resolution of public complaints efficiently without the need for crude analysis (customer surveys, product forms, etc).
Keywords: Crude analysis, natural language processing, opinion mining, sentiment analysis, social listening, twitter
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