Abstract
The Web has dramatically changed the way that customers express their opinions about some products. Everyone can post reviews/feedbacks of products at merchant sites. The huge amount of the information is a challenge to the customer's patience to read all these feedbacks. Classical sentimental classification does not find what the reviewer liked or disliked. Feature Based Summarization (FBS) techniques are being developed to exploit these sources to help companies and individuals to gain market intelligence info. In this paper, we build a new system called Fast Feature Based Summarization (FFBS) that employs the Quick-Apriori algorithm (proposed in [10]) for speeding the process of extracting the frequent product features. Moreover, in this system, we present a fast technique for finding nouns (product features) and adjectives (opinions) at the same time. These modifications speed the process of the summarizing system.