E-commerce false comment judging and recognizing method

Abstract

The invention relates to the field of internet and especially relates to an e-commerce false comment judging and recognizing method. According to the invention, comment content and comment IDs in web page data of a target commodity are captured through a client. Identical comment content is collected through a server and the similarity probability of other comment content is calculated by using a text comparison algorithm. The probability value is compared with a content similarity threshold value obtained through a machine learning machine and the similar comment content is judged. By analyzing the comment IDs, the number of the identical IDs is counted and the similarity probability of other IDs is judged. The similarity probability is compared with a comment ID similarity threshold value and the similar comment IDs are judged. According to the invention, false comments of dishonest sellers in e-commerce can be recognized and marked and corresponding false comment rates are given. Compared with a prior method, the method provided by the invention is more accurate in judging result. Users can make comprehensive consideration based on the false comment rates and the marked false comments, so that transaction risks brought by comment cheating of the sellers can be avoided.

Description

technical field [0001] The invention relates to the field of the Internet, in particular to a method for judging and identifying false e-commerce evaluations. Background technique [0002] In modern times, with the popularity of the Internet, e-commerce has become a widely used commercial trade method. Buyers and sellers mainly conduct transactions through e-commerce websites or software. Since e-commerce does not have traditional physical stores and does not require a high number of sales staff, it can control operating costs better than traditional transaction models, and thus has a greater price advantage. However, in order to improve their sales, many unscrupulous merchants hire professional reviewing teams to create a large number of false reviews to falsely promote their products, thereby deceiving consumers to increase their real sales. [0003] In order to discriminate the false evaluations forged by current merchants, the main methods in the prior art include: 1. ...

AI Technical Summary

Problems solved by technology

Similarly, the second method of discrimination also has great technical defects, because only the evaluations issued by the same ID are regarded as false evaluations, or a certain number of evaluations issued by the same ID are false evaluations; such a method of discrimination is imperfect and inaccurate of

Because today's professional appraisers will manually or use automatic registration machines to register many trumpets (the so-called trumpets refer to different ID numbers registered and used by the same person) and then use different trumpets to make false purchases of products False evaluations; in the existing identification technology, these false evaluations cannot be identified because the ID numbers used by the fakers are different

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