Multi-platform integration for classification of web content

1. A computing system comprising:

at least one programmable processor; and

a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising:

receiving a first request from at least one user device to execute an instance of an application;

transmitting a graphical user interface (GUI) to the at least one user device to be rendered on a display of the at least one user device;

receiving a second request, via the GUI, from the at least one user device, to deploy a digital advertisement, the second request including a set of platforms of a plurality of platforms of a multi-platform integration system, a set of settings, a set of parameters, a set of allocation data, and a configuration of a line associated with a campaign shell for a corresponding campaign including with the digital advertisement, the line corresponding to a customized placement of the digital advertisement configured based on a screen type of the at least one user device;

interfacing with each one of the platforms in the set of platforms;

integrating a digital advertisement directly with each one of the platforms in the set of platforms based on the set of settings, the set of parameters and the set of allocation data; and

dynamically adjusting the set of allocation data for each of the platforms,

wherein the dynamically adjusting of the set of allocation data for each of the platforms comprises:

using one or more of a URL parsing, HTML metadata, and text classification, running an in-browser script to crawl data associated with one or more sub-URL webpages of a website of each one of the platforms to create a cascading analysis of the website;

the in-browser script using machine-learning to execute in real-time a pre-trained machine learning model to process and categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for the each of the platforms, wherein the machine-learning used by the in-browser script parses elements of the sub-URL webpages, the elements including one or more of a placement of images, a text, and comments on the sub-URL webpages, and wherein the machine-learning used by the in-browser script further learns or uses contextual queues to parse a meaning of the text or comments; and

dynamically adjusting the set of allocation data for each of the platforms based on the webpage classification.

2. The system of claim 1 , wherein the set of allocation data is associated with a number of units of the digital advertisement to be deployed to each platform in the set of platforms.

3. The system of claim 2 , wherein the operations further comprise receiving a third request from the at least one user device to adjust the set of allocation data for one or more platforms of the set of platforms on which the digital advertisement has been integrated, after the digital advertisement is integrated with each one of the platforms of the set of platforms.

4. The system of claim 1 , wherein the operations further comprise interfacing with each one of the one or more platforms to adjust a number of units of the digital advertisement to be deployed on a respective platform, based on an adjustment of the set of allocation data.

5. A method comprising, at least:

receiving a first request from at least one user device to execute an instance of an application;

transmitting a graphical user interface (GUI) to the at least one user device to be rendered on a display of the at least one user device;

receiving a second request, via the GUI, from the at least one user device, to deploy a digital advertisement, the second request including a set of platforms of a plurality of platforms of a multi-platform integration system, a set of settings, a set of parameters, a set of allocation data, and a configuration of a line associated with a campaign shell for a corresponding campaign including with the digital advertisement, the line corresponding to a customized placement of the digital advertisement configured based on a screen type of the at least one user device;

interfacing with each one of the platforms in the set of platforms;

integrating a digital advertisement directly with each one of the platforms in the set of platforms based on the set of settings, the set of parameters and the set of allocation data; and

dynamically adjusting the set of allocation data for each of the platforms,

wherein the dynamically adjusting of the set of allocation data for each of the platforms comprises:

using one or more of a URL parsing, HTML metadata, and text classification, running an in-browser script to crawl data associated with one or more sub-URL webpages of a website of each one of the platforms to create a cascading analysis of the website;

the in-browser script using machine-learning to execute in real-time a pre-trained machine learning model to process and categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for the each of the platforms, wherein the machine-learning used by the in-browser script parses elements of the sub-URL webpages, the elements including one or more of a placement of images, a text, and comments on the sub-URL webpages, and wherein the machine-learning used by the in-browser script further learns or uses contextual queues to parse a meaning of the text or comments; and

dynamically adjusting the set of allocation data for each of the platforms based on the webpage classification.

6. The method of claim 5 , wherein the set of allocation data is associated with a number of units of the digital advertisement to be deployed to each platform in the set of platforms.

7. The method of claim 6 , further comprising receiving a third request from the at least one user device to adjust the set of allocation data for one or more platforms of the set of platforms on which the digital advertisement has been integrated, after the digital advertisement is integrated with each one of the platforms of the set of platforms.

8. The method of claim 5 , further comprising interfacing with each one of the one or more platforms to adjust a number of units of the digital advertisement to be deployed on a respective platform, based on an adjustment of the set of allocation data.

9. A non-transitory machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations comprising, at least:

receiving a first request from at least one user device to execute an instance of an application;

transmitting a graphical user interface (GUI) to the at least one user device to be rendered on a display of the at least one user device;

receiving a second request, via the GUI, from the at least one user device, to deploy a digital advertisement, the second request including a set of platforms of a plurality of platforms of a multi-platform integration system, a set of settings, a set of parameters, a set of allocation data, and a configuration of a line associated with a campaign shell for a corresponding campaign including with the digital advertisement, the line corresponding to a customized placement of the digital advertisement configured based on a screen type of the at least one user device;

interfacing with each one of the platforms in the set of platforms;

integrating a digital advertisement directly with each one of the platforms in the set of platforms based on the set of settings, the set of parameters and the set of allocation data, and

dynamically adjusting the set of allocation data for each of the platforms,

wherein the dynamically adjusting of the set of allocation data for each of the platforms comprises:

using one or more of a URL parsing, HTML metadata, and text classification, running an in-browser script to crawl data associated with one or more sub-URL webpages of a website of each one of the platforms to create a cascading analysis of the website;

the in-browser script using machine-learning to execute in real-time a pre-trained machine learning model to process and categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for the each of the platforms, wherein the machine-learning used by the in-browser script parses elements of the sub-URL webpages, the elements including one or more of a placement of images, a text, and comments on the sub-URL webpages, and wherein the machine-learning used by the in-browser script further learns or uses contextual queues to parse a meaning of the text or comments; and

dynamically adjusting the set of allocation data for each of the platforms based on the webpage classification.

10. The medium of claim 9 , wherein the set of allocation data is associated with a number of units of the digital advertisement to be deployed to each platform in the set of platforms.

11. The medium of claim 10 , wherein the operations further comprise receiving a third request from the at least one user device to adjust the set of allocation data for one or more platforms of the set of platforms on which the digital advertisement has been integrated, after the digital advertisement is integrated with each one of the platforms of the set of platforms.

12. The medium of claim 9 , wherein the operations further comprise interfacing with each one of the one or more platforms to adjust a number of units of the digital advertisement to be deployed on a respective platform, based on an adjustment of the set of allocation data.