Content processing automation

1. A content processing system comprising:

a computing platform including a hardware processor and a system memory;

the system memory storing a content classification software code, a natural language processor, and a computer vision analyzer;

the hardware processor configured to execute the content classification software code to:

receive a plurality of content inputs from a plurality of content sources;

for a first content input of the plurality of content inputs:

parse the first content input for metadata describing the first content input;

obtain, from the natural language processor, a description of a language-based content included in the first content input;

obtain, from the computer vision analyzer, a description of a visual content included in the first content input;

associate a plurality of predetermined annotation tags with the first content input based on the metadata, the description of the language-based content, and the description of the visual content; and

assign the first content input to a predetermined subject matter classification based on the plurality of predetermined annotation tags.

2. The content processing system of claim 1 , wherein the hardware processor is further configured to execute the content classification software code to:

perform a comparison of the first content input to other content inputs of the plurality of content inputs assigned to the predetermined subject matter classification;

generate a new annotation tag based on the comparison in an automated annotation process; and

associate the new annotation tag with at least one of the first content input or at least one of the other content inputs.

3. The content processing system of claim 1 , wherein the natural language processor comprises an artificial neural network.

4. The content processing system of claim 1 , wherein the first content input comprises video content.

5. The content processing system of claim 1 , wherein the first content input comprises social media content.

6. The content processing system of claim 1 , wherein the first content input comprises crowd sourced content.

7. The content processing system of claim 1 , wherein the first content input comprises a news feed, and wherein the predetermined subject matter classification corresponds to a news story.

8. The content processing system of claim 1 , wherein at least one of the plurality of content sources is a user of the content processing system authorized to access the predetermined subject matter classification.

9. The content processing system of claim 1 , wherein for each of a given content input of the plurality of content inputs other than the first content input, the hardware processor is further configured to execute the content classification software code to:

parse the given content input for metadata describing the given content input;

obtain, from the natural language processor, a description of a language-based content included in the given content input;

obtain, from the computer vision analyzer, a description of a visual content included in the given content input;

associate a plurality of predetermined annotation tags with the given content input based on the metadata describing the given content input, the description of the language-based content included in the given content input, and the description of the visual content included in the given content input; and

assign the given content input to a predetermined subject matter classification based on the plurality of predetermined annotation tags associated with the given content input.

10. The content processing system of claim 7 , wherein at least one of the plurality of content inputs comprises a journalistic contribution to the news story submitted by a user of the content processing system authorized to access the predetermined subject matter classification.

11. A method for use by a media content annotation system including a computing platform having a hardware processor and a system memory storing a content classification software code, a natural language processor, and a computer vision analyzer, the method comprising:

receiving, using the hardware processor and the content classification software code, a plurality of content inputs from a plurality of content sources;

for a first content input of the plurality of content inputs:

parsing the first content input, using the hardware processor and the content classification software code, for metadata describing the first content input;

obtaining, using the hardware processor and the content classification software code, from the natural language processor, a description of a language-based content included in the first content input;

obtaining, using the hardware processor and the content classification software code, from the computer vision analyzer, a description of a visual content included in the first content input;

associating, using the hardware processor and the content classification software code, a plurality of predetermined annotation tags with the first content input based on the metadata, the description of the language-based content, and the description of the visual content; and

assigning, using the hardware processor and the content classification software code, the first content input to a predetermined subject matter classification based on the plurality of predetermined annotation tags.

12. The method of claim 11 , further comprising:

performing a comparison, using the hardware processor and the content classification software code, of the first content input to other content inputs of the plurality of content inputs assigned to the predetermined subject matter classification;

generating, using the hardware processor and the content classification software code, a new annotation tag based on the comparison in an automated annotation process; and

associating, using the hardware processor and the content classification software code, the new annotation tag with at least one of the first content input or at least one of the other content inputs.

13. The method of claim 11 , wherein the natural language processor comprises an artificial neural network.

14. The method of claim 11 , wherein the first content input comprises video content.

15. The method of claim 11 , wherein the first content input comprises social media content.

16. The method of claim 11 , wherein the first content input comprises crowd sourced content.

17. The method of claim 11 , wherein the first content input comprises a news feed, and wherein the predetermined subject matter classification corresponds to a news story.

18. The method of claim 11 , wherein at least one of the plurality of content sources is a user of the content processing system authorized to access the predetermined subject matter classification.

19. The method of claim 11 , further comprising:

for each of a given content input of the plurality of content inputs other than the first content input:

parsing the given content input for metadata describing the given content input;

obtaining, from the natural language processor, a description of a language-based content included in the given content input;

obtaining, from the computer vision analyzer, a description of a visual content included in the given content input;

associating a plurality of predetermined annotation tags with the given content input based on the metadata describing the given content input, the description of the language-based content included in the given content input, and the description of the visual content included in the given content input; and

assigning the given content input to a predetermined subject matter classification based on the plurality of predetermined annotation tags associated with the given content input.

20. The method of claim 17 , wherein at least one of the plurality of content inputs comprises a journalistic contribution to the news story submitted by a user of the content processing system authorized to access the predetermined subject matter classification.