Fine-grained sentiment analysis of social media with emotion sensing

Social media is arguably the richest source of human generated text input. Opinions, feedbacks and critiques provided by internet users reflect attitudes and sentiments towards certain topics, products, or services. The sheer volume of such information makes it effectively impossible for any group of persons to read through. Thus, social media sentiment analysis has become an important area of work to make sense of the social media talk. However, most existing sentiment analysis techniques focus only on the aggregate level, classifying sentiments broadly into positive, neutral or negative, and lack the capabilities to perform fine-grained sentiment analysis. This paper describes a social media analytics engine that employs a social adaptive fuzzy similarity-based classification method to automatically classify text messages into sentiment categories (positive, negative, neutral and mixed), with the ability to identify their prevailing emotion categories (e.g., satisfaction, happiness, excitement, anger, sadness, and anxiety). It is also embedded within an end-to-end social media analysis system that has the capabilities to collect, filter, classify, and analyze social media text data and display a descriptive and predictive analytics dashboard for a given concept. The proposed method has been developed and is ready to be licensed to users.