No abstract

Now a days online consumer review is most powerful tool for decision making. This term serve as electronic word of mouth (EWM) which become an increasingly popular. Millions of people are now buy products and services via online. Web services are provided feature to users openly. The web can provides an extensive source of consumer reviews. The user can read all the reviews and evaluate fair view about product or service. This can applicable only for limited number of reviews presented on web. The web contain more than hundreds of reviews then problem arrived and time consuming also. A text processing framework is desirable which summarize all the reviews. This framework would be find out general aspect category addressed in all review sentences. The method presented in this framework which applies association rule mining on co-occurrence frequency data to find out these aspect categories. From this result, generate polarity score for each aspect category. This polarity score helps to evaluate fair decision making for customer as well as company.