Extracting Emotion Quotient of Viral Information Over Twitter

In social media platforms, a viral information or trending term draws attention, as it asserts potential user content towards topic/terms and sentiment flux. In real-time sentiment analysis, this viral information deliver potential insights, as encompass sentiment and co-located ranges of emotions be useful for the analysis and decision support. A traditional sentiment analysis tool generates the level of predefined sentiments over socialmedia content for the defined duration and lacks in the extraction of emotional impact created by the same. In these settings, it is a multifaceted task to estimate precisely the emotional quotient viral information creates. The proposed novel algorithm aims, to (i) extract the sentiment and co-located emotions quotient of viral information and (ii) utilities for comprehensive comparison on co-occurring viral informations, and sentiment analysis over Twitter text data. The generated emotion quotients and micro-sentiment reveals several valuable insight of a viral topic and assists in decision support. Ause-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also.