Performance metric prediction and content item text suggestion based upon content item text

1. A method, comprising:

displaying a content item text interface via a client device;

receiving, via the content item text interface, a first set of content item text;

determining, based upon the first set of content item text, a first performance metric score;

determining, based upon the first set of content item text and a plurality of sets of content item text associated with a plurality of content items, a plurality of similarity scores associated with the plurality of sets of content item text, wherein:

the plurality of sets of content item text comprises a second set of content item text of a first content item of the plurality of content items; and

a first similarity score of the plurality of similarity scores is associated with a similarity between the first set of content item text and the second set of content item text;

selecting, based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text, one or more sets of content item text from among the plurality of sets of content item text, wherein:

the plurality of performance metric scores comprises one or more performance metric scores associated with the one or more sets of content item text; and

the one or more performance metric scores are higher than the first performance metric score;

determining, based upon the first performance metric score and the one or more performance metric scores associated with the one or more sets of content item text, a text strength classification of the first set of content item text, wherein the text strength classification corresponds to a first classification based upon a determination of whether the one or more performance metric scores meet a threshold performance metric score based upon the first performance metric score; and

displaying, via the client device, at least one of one or more representations of the one or more sets of content item text or an indication of the text strength classification.

2. The method of claim 1 , wherein:

the determining the first performance metric score is performed using a first machine learning model.

3. The method of claim 2 , comprising:

training a machine learning model using first training data to generate the first machine learning model, wherein the first training data comprises a second plurality of sets of content item text associated with a second plurality of content items and a plurality of sets of content event information associated with the second plurality of content items.

4. The method of claim 3 , wherein:

the plurality of sets of content event information comprises a first set of content event information associated with a second content item; and

the first set of content event information is indicative of first content event information associated with first content events performed via one or more first internet resources corresponding to a first entity, wherein a content event of the first content events corresponds to presentation of the second content item via an internet resource of the one or more first internet resources.

5. The method of claim 4 , comprising:

receiving, via the content item text interface, an indication of the first entity, wherein the determining the first performance metric score is performed based upon the first entity.

6. The method of claim 3 , wherein:

the first machine learning model comprises at least one of a linear model, a logistic regression model, a naïve Bayes logistic regression (NBLR) model or a deep learning model.

7. The method of claim 1 , comprising:

determining a first embedding-based representation of the first set of content item text, wherein the determining the plurality of similarity scores comprises determining the first similarity score based upon the first embedding-based representation and a second embedding-based representation of the second set of content item text of the first content item.

8. The method of claim 1 , wherein:

the displaying the one or more representations of the one or more sets of content item text is performed responsive to a determination that each performance metric score of the one or more performance metric scores meets the threshold performance metric score.

9. The method of claim 1 , wherein the selecting the one or more sets of content item text from among the plurality of sets of content item text comprises:

selecting, based upon the plurality of similarity scores, a second plurality of sets of content item text from among the plurality of sets of content item text based upon a determination that the second plurality of sets of content item text are associated with highest similarity scores of the plurality of similarity scores; and

selecting the one or more sets of content item text from among the second plurality of sets of content item text based upon a determination that each performance metric score of the one or more performance metric scores meets the threshold performance metric score.

10. The method of claim 1 , comprising:

displaying one or more indications of the one or more performance metric scores associated with the one or more sets of content item text.

11. The method of claim 1 , comprising:

displaying one or more indications that the one or more performance metric scores associated with the one or more sets of content item text are higher than the first performance metric score.

12. The method of claim 1 , wherein:

the displaying the indication of the text strength classification is performed responsive to a determination that each performance metric score of the one or more performance metric scores meets the threshold performance metric score.

13. The method of claim 1 , comprising:

receiving a third set of content item text;

determining, based upon the third set of content item text, a second performance metric score;

determining, based upon the third set of content item text and a second plurality of sets of content item text associated with a second plurality of content items, a second plurality of similarity scores associated with the second plurality of sets of content item text, wherein:

the second plurality of sets of content item text comprises a fourth set of content item text of a second content item of the second plurality of content items; and

a second similarity score of the second plurality of similarity scores is associated with a similarity between the third set of content item text and the fourth set of content item text;

selecting, based upon the second plurality of similarity scores, a third plurality of sets of content item text from among the second plurality of sets of content item text based upon a determination that the third plurality of sets of content item text are associated with highest similarity scores of the second plurality of similarity scores;

comparing a second threshold performance metric score with performance metric scores associated with the third plurality of sets of content item text to determine whether the performance metric scores comprise a performance metric score that meets the second threshold performance metric score, wherein the second threshold performance metric score is based upon the second performance metric score; and

determining, based upon the comparing the second threshold performance metric score with the performance metric scores, a second text strength classification of the third set of content item text, wherein:

the second text strength classification corresponds to a second classification based upon a determination that the performance metric scores do not comprise a performance metric score that meets the second threshold performance metric score; and

the second classification is different than the first classification.

14. The method of claim 13 , comprising:

displaying an indication of the second text strength classification.

15. A computing device comprising:

a processor; and

memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:

displaying a content item text interface via a client device;

receiving, via the content item text interface, a first set of content item text;

determining, based upon the first set of content item text, a first performance metric score;

determining, based upon the first set of content item text and a plurality of sets of content item text associated with a plurality of content items, a plurality of similarity scores associated with the plurality of sets of content item text, wherein:

the plurality of sets of content item text comprises a second set of content item text of a first content item of the plurality of content items; and

a first similarity score of the plurality of similarity scores is associated with a similarity between the first set of content item text and the second set of content item text;

selecting, based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text, one or more sets of content item text from among the plurality of sets of content item text, wherein:

the plurality of performance metric scores comprises one or more performance metric scores associated with the one or more sets of content item text; and

the one or more performance metric scores are higher than the first performance metric score;

determining, based upon the first performance metric score and the one or more performance metric scores associated with the one or more sets of content item text, a text strength classification of the first set of content item text, wherein the text strength classification corresponds to a first classification based upon a determination of whether the one or more performance metric scores meet a threshold performance metric score based upon the first performance metric score; and

displaying, via the client device, at least one of one or more representations of the one or more sets of content item text or an indication of the text strength classification.

16. The computing device of claim 15 , wherein:

the determining the first performance metric score is performed using a first machine learning model.

17. The computing device of claim 16 , the operations comprising:

training a machine learning model using first training data to generate the first machine learning model, wherein the first training data comprises a second plurality of sets of content item text associated with a second plurality of content items and a plurality of sets of content event information associated with the second plurality of content items.

18. The computing device of claim 17 , wherein:

the plurality of sets of content event information comprises a first set of content event information associated with a second content item; and

the first set of content event information is indicative of first content event information associated with first content events performed via one or more first internet resources corresponding to a first entity, wherein a content event of the first content events corresponds to presentation of the second content item via an internet resource of the one or more first internet resources.

19. The computing device of claim 18 , the operations comprising:

receiving, via the content item text interface, an indication of the first entity, wherein the determining the first performance metric score is performed based upon the first entity.

20. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

receiving, from a client device, a first set of content item text;

determining, based upon the first set of content item text, a first performance metric score;

determining, based upon the first set of content item text and a plurality of sets of content item text associated with a plurality of content items, a plurality of similarity scores associated with the plurality of sets of content item text, wherein:

the plurality of sets of content item text comprises a second set of content item text of a first content item of the plurality of content items; and

a first similarity score of the plurality of similarity scores is associated with a similarity between the first set of content item text and the second set of content item text;

selecting, based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text, one or more sets of content item text from among the plurality of sets of content item text, wherein:

the plurality of performance metric scores comprises one or more performance metric scores associated with the one or more sets of content item text; and

the one or more performance metric scores are higher than the first performance metric score;

determining, based upon the first performance metric score and the one or more performance metric scores associated with the one or more sets of content item text, a text strength classification of the first set of content item text, wherein the text strength classification corresponds to a first classification based upon a determination of whether the one or more performance metric scores meet a threshold performance metric score based upon the first performance metric score; and

displaying, via the client device, at least one of one or more representations of the one or more sets of content item text or an indication of the text strength classification.