Potential Power and Problems in Sentiment Mining of Social Media

Authors:
John Wang;Qiannong Gu;Gang Wang
Affiliations:
Department of Information & Operations Management, Montclair State University, Montclair, NJ, USA;Department of Information Systems and Operations Management, Miller College of Business, Ball State University, Muncie, IN, USA;Department of Global Management Studies, Nathan Weiss Graduate College, Kean University, Union, NJ, USA
Venue:
International Journal of Strategic Decision Sciences
Year:
2013

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Abstract

Sentiment mining research has experienced an explosive growth in awareness and demand as Web 2.0 technologies have paved the way for a surge of social media platforms that have significantly and rapidly increased the availability of user generated opinioned text. The power of opinions has long been known and is beginning to be tapped to a fuller potential through sentiment mining research. Social media sites have become a paradise for sentiment providing endless streams of opinioned text encompassing an infinite array of topics. With the potential to predict outcomes with a relative degree of accuracy, sentiment mining has become a hot topic not only to researchers, but to corporations as well. As the social media user base continues to expand and as researchers compete to fulfill the demand for sentiment analytic tools to sift through the endless stream of user generated content, the growth of sentiment mining of social media will continue well into the future with an emphasis on improved reliability, accuracy, and automation.