Along line of research has shown the vital significance of recognizing and observing company’s contestants. In the framework of this activity various questions are emerge like: In what way we justify and measure the competitiveness between two items? Who are the most important competitors of a specified item? What are the various features of an item that act on competitiveness? Inspired by this issue, the advertising and administration group have concentrated on observational strategies for competitor distinguishing proof and in addition on techniques for examining known contenders. Surviving examination on the previous has concentrated on mining near articulations (e.g.one product is superior then other product) from the web or other documentary sources. Despite the fact that such articulations can without a doubt be indications of strength, they are truant in numerous spaces. By surveying the various papers, we found the conclusion of basic significance of the competitiveness between two items on the basis of market sectors. In this paper, we state novel description of the competitiveness between two items, based on the market sector. This system estimation of competitiveness uses customer reviews of different domains, a plentiful source of information. This system shows an efficient approach for evaluating competitiveness in large review datasets and finding the top-k competitors. Our experiments are based on a corpus of Yelp.in, TripAdvisor.com, and Amazon customer reviews which states that the proposed methodology can extract comparative relations more precisely. In this paper, we state an efficient framework for the classification of reviews of mainstream domain using k-means clustering and Naïve Bayes algorithm. This system evaluates the competitiveness of two items from frequent item set to find top-k competitor using Apriori algorithm.

Data mining, Web mining, Information Search and Retrieval, K_means clustering, Naïve Bayes Classifier, Rule mining.

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