Recent growth and development of big social media platforms like Twitter, Facebook,Instagram etc. and personal blog sites generate huge amount of unstructured data. Analysis of this data may provide insights into the opinion of people as well as their feelings towards certain subjects, products or services. The process of extracting valuable data from opinions of people to assess their feelings and thoughts is known as opinion mining. Mining the opinions of people has applications in several areas: understanding what people like or dislike is critical for making informed business and political decisions. In this work, we focus on Opinion Mining from Text to suggest Actionable Recommendations. The Actionable Patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state. We apply our method to Twitter Social Network Data, as well as Student Survey Education data. We aim to suggest ways to improve the Social Network Experience and to improve Teaching Methods and Student Learning. We implement and test our system in Scalable Environment with BigData using the Apache Spark platform.