Tone optimization for digital content

1. A method comprising:

obtaining digital content;

using natural language processing and a trained tone prediction model to obtain a score from a reasoning algorithm, wherein the trained tone prediction model comprises capturing reasoning algorithms using trained models;

using the trained tone prediction model to weigh the score and infer a current tone of the digital content;

analyzing target audience content associated with a target audience to obtain a target audience tone;

using the target audience tone to derive a desired tone for the target audience;

creating, by a tone optimization generator, a linguistic tone optimization recommendation for the target audience to reduce a difference between the current tone and the desired tone, wherein the linguistic tone optimization recommendation includes a prioritized list of a plurality of linguistic modification suggestions, and wherein the tone optimization generator uses a correlation learned from the trained tone prediction model; and

outputting the linguistic tone optimization recommendation to an interactive user interface that allows for iterative and selective implementation of the linguistic modification suggestions.

2. The method of claim 1 , wherein the digital content is obtained as input from a user, and wherein the iterative and selective implementation is done at a word level.

3. The method of claim 1 , further comprising:

outputting an analysis of a plurality of tones inferred by the digital content.

4. The method of claim 1 , wherein the interactive user interface is used to display an explanation for the current tone.

5. The method of claim 1 , further comprising:

outputting an analysis of a plurality of tone types inferred by the digital content.

6. The method of claim 1 , further comprising:

graphically displaying the current tone in a tone graph.

7. The method of claim 1 , wherein the target audience comprises a plurality of entities, and wherein the target audience content comprises a plurality of digital footprints.

8. The method of claim 1 , wherein the desired tone is derived using an expected mean of previous tones.

9. A computer program product stored in a non-transitory computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to provide a tone optimization recommendation by performing actions comprising:

obtaining digital content;

using natural language processing and a trained tone prediction model to obtain a score from a reasoning algorithm, wherein the trained tone prediction model comprises capturing reasoning algorithms using trained models;

using the trained tone prediction model to weigh the score and infer a current tone of the digital content;

analyzing target audience content associated with a target audience to obtain a target audience tone;

using the target audience tone to derive a desired tone for the target audience;

creating, by a tone optimization generator, a linguistic tone optimization recommendation for the target audience to reduce a difference between the current tone and the desired tone, wherein the linguistic tone optimization recommendation includes a prioritized list of a plurality of linguistic modification suggestions, and wherein the tone optimization generator uses a correlation learned from the trained tone prediction model; and

outputting the linguistic tone optimization recommendation to an interactive user interface that allows for iterative and selective implementation of the linguistic modification suggestions.

10. The computer program product of claim 9 , wherein the digital content comprises an aggregation of a plurality of digital footprints.

11. The computer program product of claim 9 , further comprising:

outputting an analysis of a plurality of tones inferred by the digital content.

12. The computer program product of claim 9 , wherein the interactive user interface is used to display an explanation for the current tone.

13. The computer program product of claim 9 , further comprising:

outputting an analysis of a plurality of tone types inferred by the digital content.

14. The computer program product of claim 9 , further comprising:

graphically displaying the current tone in a tone graph.

15. The computer program product of claim 9 , wherein the desired tone is derived using content created by the target audience.

16. The computer program product of claim 9 , wherein the target audience content comprises an aggregation of a plurality of digital footprints.

17. The computer program product of claim 9 , further comprising:

obtaining another desired tone, wherein the another desired tone includes another desired tone for another target audience;

creating another tone optimization recommendation to reduce a difference between the current tone and the another desired tone; and

outputting the another tone optimization recommendation via the interactive user interface.

18. A system comprising:

one or more processors;

a memory coupled to at least one of the processors; and

a set of computer program instructions stored in the memory and executed by at least one of the processors to perform the actions of:

obtaining digital content;

using natural language processing and a trained tone prediction model to obtain a score from a reasoning algorithm, wherein the trained tone prediction model comprises capturing reasoning algorithms using trained models;

using the trained prediction model to weigh the score and infer a current tone of the digital content;

analyzing target audience content associated with a target audience to obtain a target audience tone;

using the target audience tone to derive a desired tone for the target audience;

creating, by a tone optimization generator, a linguistic tone optimization recommendation for the target audience to reduce a difference between the current tone and the desired tone, wherein the linguistic tone optimization recommendation includes a prioritized list of a plurality of linguistic modification suggestions, and wherein the tone optimization generator uses a correlation learned from the trained tone prediction model; and

outputting the linguistic tone optimization recommendation to an interactive user interface that allows for iterative and selective implementation of the linguistic modification suggestions.

19. The system of claim 18 , wherein the set of computer program instructions stored in the memory and executed by at least one of the processors to perform additional actions of:

visually depicting the current tone and the linguistic tone optimization recommendation.

20. The system of claim 18 , wherein the set of computer program instructions stored in the memory and executed by at least one of the processors to perform additional actions of:

outputting an explanation for the current tone.