In Electronic commerce applications, the feedback ratings given by the customers are combined to calculate and assign seller's rating or their reputation trust score. The most widely used method in e-commerce applications to do this is via reputation trust models. But in such models the all good reputation problem is very commonly occurring, resulting in high trust scores or rating for all the sellers and this in turn makes it highly tricky for possible customers to select trustworthy and reliable sellers. In this paper, we propose a system for trust score calculation by mining feedback comments, on the basis of observation that the customers tend to express their views and opinions freely in the feedback comments section. The main objective is to implement a multidimensional trust model [1] for calculating the seller's reputation score from the information in the customer feedback comments and an algorithm for mining the text comments for dimension ratings and weights [1], using some techniques from natural language processing and from opinion mining [1] along with topic modelling. Comment based trust evaluation can successfully tackle the all good reputation problem and rank sellers efficiently, this has been revealed by previous experiments on eBay and Amazon data.

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Electronic Commerce Text Mining Natural Language Processing Opinion Mining