Text multi-vector representation mutual learning-based spam comment filter method

Abstract

The invention discloses a text multi-vector representation mutual learning-based spam comment filter method. The method comprises the following steps of: firstly preprocessing a comment text of a training set, and carrying out vectorized representation on a same text by using multiple different text vector representation methods; training multiple different classifiers by adoption of a same classifier training method according to the different vector representation manners; carrying out mutual learning among the classifier by means of a thought of cooperative training, continuously labelling data of an addition set and moving the labelled data into the training set; and finally obtaining a plurality of classifiers, the abilities of which are enhanced. According to the method, the thought of cooperative training is adopted, and automatic labelling can be carried out on residual data through manually labelling a part of data, so that a great deal of manual data labelling work is decreased, the classification ability of the classifiers is enhanced and then the spam comment filter precision is improved.

Description

technical field [0001] The invention relates to a spam comment filtering technology, in particular to a spam comment filtering method based on mutual learning of text multi-vector representations. Background technique [0002] With the rapid development of e-commerce, the number of online user reviews in major e-commerce and related fields has increased dramatically, and these reviews are also important reference information for people when shopping. In short, positive reviews will promote consumers' purchase intention, while negative reviews will largely increase consumers' doubts. Therefore, a large number of organizations and individuals appear on the Internet to use comments to cheat, create spam comments to confuse the public, and mislead users. Although most websites will set up a voting mechanism for "helpful" or "useful" on the comment content, there are very few actual voting records. Therefore, it is particularly important to filter spam comments, purify the netw...

AI Technical Summary

Problems solved by technology

Although machine learning-based methods require less labor costs, if you want to have good results, you also need a lot of manually labeled data, and the performance of traditional semi-supervised learning methods is not satisfactory.

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