Model-based classification of content items

1. A method, comprising:

obtaining validated training data comprising a first set of content items and a first set of classification tags for the first set of content items;

using the validated training data to produce, by one or more computer systems, a statistical model for classifying content using a set of dimensions represented by the first set of classification tags;

using the statistical model to generate, by the one or more computer systems, a second set of classification tags for a second set of content items which includes generating two or more classification tags from the first set of classification tags for at least one content item in the second set of content items; and

outputting, by the one or more computer systems, one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.

2. The method of claim 1 , further comprising:

obtaining a validated subset of the second set of classification tags for the second set of content items.

3. The method of claim 2 , further comprising:

providing the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

using the update to generate a third set of classification tags for a third set of content items.

4. The method of claim 2 , wherein obtaining the validated subset of the second set of classification tags comprises:

displaying the second set of content items and the second set of classification tags in a user interface; and

obtaining one or more corrections to the second set of classification tags through the user interface.

5. The method of claim 1 , wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises:

generating a set of features from a content item in the first set of content items; and

providing the set of features as input to the statistical model.

6. The method of claim 5 , wherein the set of features comprises at least one of:

one or more n-grams from the content items;

a number of characters;

a number of units of speech;

an average number of units of speech; and

a percentage of a character type.

7. The method of claim 5 , wherein the set of features comprises profile data for a creator of the content item.

8. The method of claim 1 , wherein the set of dimensions comprises a sentiment.

9. The method of claim 1 , wherein the set of dimensions comprises a product associated with an online professional network.

10. The method of claim 1 , wherein the set of dimensions comprises a value proposition.

11. An apparatus, comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

obtain validated training data comprising a first set of content items and a first set of classification tags for the first set of content items;

use the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags;

use the statistical model to generate a second set of classification tags for a second set of content items, which includes generating two or more classification tags from the first set of classification tags for at least one content item in the second set of content items; and

output one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.

12. The apparatus of claim 11 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

obtain a validated subset of the second set of classification tags for the first set of content items;

provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

use the update to generate a third set of classification tags for a third set of content items.

13. The apparatus of claim 12 , wherein obtaining the validated subset of the first set of classification tags comprises:

displaying the second set of content items and the second set of classification tags in a user interface; and

obtaining one or more corrections to the second set of classification tags through the user interface.

14. The apparatus of claim 11 , wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises:

generating a set of features from a content item in the first set of content items; and

providing the set of features as input to the statistical model.

15. The apparatus of claim 14 , wherein the set of features comprises at least one of:

one or more n-grams from the content items;

a number of characters;

a number of units of speech;

an average number of units of speech;

a percentage of a character type; and

profile data for a creator of the content item.

16. The apparatus of claim 11 , wherein the set of dimensions comprises a sentiment.

17. The apparatus of claim 11 , wherein the set of dimensions comprises a product associated with an online professional network.

18. The apparatus of claim 11 , wherein the set of dimensions comprises a value proposition.

19. A system, comprising:

an analysis non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to:

obtain validated training data comprising a first set of content items and a first set of classification tags for the first set of content items;

use the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags; and

use the statistical model to generate a second set of classification tags for a second set of content items, which includes generating two or more classification tags from the first set of classification tags for at least one content item in the second set of content items; and

a management non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to output one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.

20. The system of claim 19 , wherein the analysis non-transitory computer-readable medium further instructions that, when executed by the one or more processors, cause the system to:

obtain a validated subset of the second set of classification tags for the first set of content items;

provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

use the update to generate a third set of classification tags for a third set of content items.