Multi-platform integration for classification of web content

1. A system comprising:

at least one programmable processor; and

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

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

receive a request, via the GUI, from the at least one user device, to deploy a digital advertisement, the request including a set of platforms of a plurality of platforms and a set of allocation data;

integrate a digital advertisement directly with each of the platforms in the set of platforms based on the set of allocation data; and

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

wherein the dynamic adjustment of the set of allocation data for each of the platforms comprises:

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

the in-browser script executing in real-time a machine learning model to categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for each of the platforms, wherein 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 1 , wherein, when executed by the at least one programmable processor, the processor-executable instructions cause the at least one processor to:

receive a second 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 request includes a configuration of a line associated with a campaign shell for a corresponding campaign including 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.

5. The system of claim 1 , wherein when executed by the at least one programmable processor, the processor-executable instructions cause the at least one processor to:

interface with one or more of the set of platforms to adjust a number of units of the digital advertisement to be deployed on a particular platform, based on an adjustment of the set of allocation data.

6. The system of claim 1 , wherein the machine learning model is a pre-trained model that is further trained in a browser running the in-browser script.

7. The system of claim 1 , wherein the machine learning model categorizes the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for one or more webpages of a website of one or more of the set of platforms.

8. The system of claim 1 , wherein, when executed by the at least one programmable processor, the processor-executable instructions cause the at least one processor to:

collect the crawled data using URL crawler that builds an index of the crawled data using at least one of one or more content categories and keyword counts per document.

9. The system of claim 1 , wherein, when executed by the at least one programmable processor, the processor-executable instructions cause the at least one processor to:

collect the crawled data using a wrapper that invokes executable links repeatedly and a thread-safe queue that ensures only a fixed number of linked downloads are running concurrently.

10. The system of claim 9 , wherein, when executed by the at least one programmable processor, the processor-executable instructions cause the at least one processor to:

use the thread-safe queue to stop a linked download that exceeds a specified limit; and

give a slot in the queue previously filled by the stopped download to a new linked download process.

11. A method comprising:

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

receiving a request, via the GUI, from the at least one user device, to deploy a digital advertisement, the request including a set of platforms of a plurality of platforms and a set of allocation data;

integrating a digital advertisement directly with each of the platforms in the set of platforms based on 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:

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

the in-browser script executing in real-time a machine learning model to categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for each of the platforms, wherein 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.

12. The method of claim 11 , 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.

13. The method of claim 11 , further comprising receiving a second 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.

14. The method of claim 11 , wherein the request includes a configuration of a line associated with a campaign shell for a corresponding campaign including 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.

15. The method of claim 11 , further comprising interfacing with one or more of the set of platforms to adjust a number of units of the digital advertisement to be deployed on a particular platform, based on an adjustment of the set of allocation data.

16. The method of claim 11 , wherein the machine learning model is a pre-trained model that is further trained in a browser running the in-browser script.

17. The method of claim 11 , wherein the machine learning model categorizes the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for one or more webpages of a website of one or more of the set of platforms.

18. The method of claim 11 , further comprising collecting the crawled data using URL crawler that builds an index of the crawled data using at least one of one or more content categories and keyword counts per document.

19. The method of claim 11 , further comprising collecting the crawled data using a wrapper that invokes executable links repeatedly and a thread-safe queue that ensures only a fixed number of linked downloads are running concurrently.

20. The method of claim 19 , further comprising using the thread-safe queue to stop a linked download that exceeds a specified limit; and

giving a slot in the queue previously filled by the stopped download to a new linked download process.