Advertisement creativity classification method and system

Abstract

The embodiment of the invention provides an advertisement creativity classification method and system. The method comprises the steps of acquiring creativity copywriting and creativity pictures of to-be-classified advertisement creativity; carrying out feature extraction on the creative copywriting by adopting a convolutional neural network, and determining corresponding text features; carrying out feature extraction on the creative picture by adopting the convolutional neural network, and determining corresponding picture features; splicing and combining the text features and the picture features together to obtain advanced features; and determining the industry category of the to-be-classified advertisement creativity according to the advanced features. The method performs multi-mode document modeling based on the creative copywriting and the creative picture; the creative copywriting and the creative picture are taken as references; a convolutional neural network is adopted to perform feature extraction to obtain text features and picture features, it is determined that the text features and the picture features are spliced and combined together to obtain advanced features, thenclassification is performed according to the advanced features, the industry category of the to-be-classified advertisement creativity can be obtained, and the classification accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of Internet advertisements, in particular to a method and system for classifying advertisement creativity. Background technique [0002] Programmatic advertising is already one of the important sources of income for many large Internet companies. When advertisers place advertisements on the advertising platform, they need to fill in information such as the industry to which the advertisement belongs, and the advertising platform will conduct special reviews of advertisements in specific industries (for example, whether there are false publicity, vulgarity, etc.). In order to avoid review, some advertisers will deliberately fill in false industry information. Manually identifying whether the industry information filled in by advertisers has obvious disadvantages (high labor costs and poor real-time performance), so it is necessary to have an automated advertising creative classification scheme to identify re...

AI Technical Summary

Problems solved by technology

The problem is that the classification of advertising creatives based solely on copywriting or pictures does not use all the information of this creative, which is equivalent to losing some features. Due to the different emphasis of information conveyed by text and pictures, the accuracy of classification Difference

[0006] 2) Most of the existing classification models are based on statistical methods

[0008] The existing advertising creative classification methods cannot effectively combine the copywriting and matching pictures in the advertising creative, making the classification accuracy relatively low

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