International Journal of Computer Science and Business Informatics IJCSBI.ORG ISSN: 1694-2108 | Vol. 13, No. 1. MAY 2014 45 A Survey on Knowledge Analytics of Text from Social Media Dr. J. Akilandeswari Professor and Head, Department of Information Technology Sona College of Technology, Salem, India. K. Rajalakshm PG Scholar, Department of Information Technology Sona College of Technology, Salem, India. ABSTRACT Actionable knowledge discovery is a closed optimization problem solving process from problem definition. It is used to extract the actionable data that are usable. Social media still contain many comments that cannot be directly acted upon. If we could automatically filter out such noise and only present actionable comments, decision making process will be easier. Automatically extracting actionable knowledge from on line social media has been attracted a growing interest from both academia and the industry. This paper gives a study in the systems and methods available text from the social media like twitter or Facebook. Keywords knowledge discovery, social networking, classification. 1. INTRODUCTION Social networking becomes one of the most important parts of our daily life. It enables us to communicate with a lot of people. Social networking is created to assist in online networking. These social sites are generally communities created to support a common idea. Data mining is the process of discovering actionable information from large sets of data. Actionable knowledge discovery from user-generated content is a commodity much sought after by industry and market research. The value of user-generated content varies significantly from excellence to abuse. As the availability of such content increases, identifying high-quality content in social sites based on user contributions is very difficult. Social media sites become increasingly important. In general social media demonstrate a rich variety of information sources. In addition to the content itself, there is a large array of non-content information obtainable in these sites, such as links between items and unambiguous quality ratings from members of the community. We argue that to achieve the goal we must gain a better understanding of what actionable knowledge is, where it can be found and what kind of