1. A system to extract customer feedback from a microblog site, said system comprising a database, a processor and one or more automated tools, further wherein said one or more automated tools comprise: a retrieval unit to capture microblog updates posted by customers from the microblog site; a filter unit to filter the captured microblog updates according to filter criteria that remove non-actionable items from the captured microblog updates; a learning unit to prioritize the filtered microblog updates, wherein said prioritizing comprises assigning probabilities to corresponding filtered microblog updates, said assigning of probabilities based on popularity of each of the customers based on number of followers of said customer on said first microblog site, results from monitoring one or more microblog sites different from said first microblog site for one or more postings made by the customers, further wherein each said assigned probability is used to indicate confidence that the corresponding filtered microblog update is actionable, and assigning priorities to said corresponding filtered microblog updates based on said assigned probabilities, storing the filtered microblog updates in the database after assigning said priorities, tagging the filtered microblog updates in the database based on the corresponding assigned priorities, discarding at least one of the filtered microblog updates based on said tagging, wherein said discarding is based on an intelligent dropping policy and congestion levels; and a classification unit to classify the filtered and prioritized microblog updates in the database wherein said classifying performed by the classification unit comprises selecting at least some of the prioritized microblog updates from the database based on said tagging, and determining whether each selected microblog update is actionable.