Digital Social Network User Prediction By Using Fuzzy Logic Source Code Download

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       Overview

In our daily life, customers are most likely to buy online products with highly-praised reviews. Item reputation is one of the important factor which reflects customer’s comprehensive evaluation based on the intrinsic value of the specific product. The item is to be with the bad reputation, if the item’s reviews is full of negative reviews. We must infer the reputation and comprehensive rating to know about the user sentiment for an item. Per the customer’s perspective both positive and negative reviews are required as a reference. For a positive review, we will know about the advantages of the product and for a negative review we will obtain the shortcomings in the case of being cheated. So, it is worth to explore those reviewers who have obvious and objective attitude on items. The other customers will get influenced by those reviewers if a reviewer has clear like and dislike sentiment so that the other users will pay much attention to his/him considerations. However, user’s sentiment is hard to predict and unpredictability of interpersonal sentimental influence makes great difficulty in exploring social users [11]. Moreover, most of the user support only exact keyword search which greatly affects data usability and user experience.

Proposed Methodology

In this section proposed work is explained in detail with the help of block diagram. Here block diagram show steps of proposed work. Same thing is again explain by the proposed work algorithm. Work has included different formula with example of its input and output. Explanation of some dataset format is also discussed as pre-processing of those is done in proposed work. Whole work is divide into two model first is filtering of paid users from the dataset. Here those users who are highly frequent and make rating which are quit larger than the normal or quit lower than the normal deviation of the service rating. Second model study the rating behaviors of the true user from the dataset

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