Systems, methods, and non-transitory computer-readable media according to certain aspects can obtain a comment submitted by a user on a page associated with an entity. A training data set, including a plurality of comments, that indicates whether each of the plurality of comments is actionable can be determined. A machine learning model can be trained based on the training data set. Whether the comment is actionable can be determined based at least in part on the machine learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: obtaining, by a computing system, a comment submitted by a user on a page associated with an entity; determining, by the computing system, a training data set, including a plurality of comments, that indicates whether each of the plurality of comments is actionable; training, by the computing system, a machine learning model based on the training data set; and determining, by the computing system, whether the comment is actionable based at least in part on the machine learning model. 2. The computer-implemented method of claim 1, wherein the determining whether the comment is actionable comprises associating the comment with a first classification in response to determining that the comment is actionable, the first classification indicative of a comment being actionable. 3. The computer-implemented method of claim 2, wherein the machine learning model provides the first classification and a confidence score associated with the first classification. 4. The computer-implemented method of claim 3, wherein the first classification is associated with the comment when the confidence score associated with the first classification satisfies a threshold value. 5. The computer-implemented method of claim 3, further comprising displaying the first classification in a user interface associated with the page when the confidence score associated with the first classification satisfies a threshold value. 6. The computer-implemented method of claim 2, further comprising receiving user input relating to whether the first classification is correct. 7. The computer-implemented method of claim 2, wherein a comment that is actionable in the training data set is associated with the first classification. 8. The computer-implemented method of claim 2, wherein the determining the training data set comprises performing a pattern search on one or more comments using one or more regular expressions, wherein each of the one or more regular expressions is associated with the first classification, and wherein a comment of the one or more comments that includes text matching at least one of the one or more regular expressions is associated with the first classification and included in the training data set. 9. The computer-implemented method of claim 2, wherein the determining the training data set comprises obtaining one or more comments that are associated with the first classification based at least in part on human input. 10. The computer-implemented method of claim 1, further comprising determining an intent classification for the comment, the intent classification indicative of an intent associated with a comment. 11. A system comprising: at least one hardware processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: obtaining a comment submitted by a user on a page associated with an entity; determining a training data set, including a plurality of comments, that indicates whether each of the plurality of comments is actionable; training a machine learning model based on the training data set; and determining whether the comment is actionable based at least in part on the machine learning model. 12. The system of claim 11, wherein the determining whether the comment is actionable comprises associating the comment with a first classification in response to determining that the comment is actionable, the first classification indicative of a comment being actionable. 13. The system of claim 12, wherein the machine learning model provides the first classification and a confidence score associated with the first classification. 14. The system of claim 13, wherein the first classification is associated with the comment when the confidence score associated with the first classification satisfies a threshold value. 15. The system of claim 13, wherein the instructions further cause the system to perform displaying the first classification in a user interface associated with the page when the confidence score associated with the first classification satisfies a threshold value. 16. A non-transitory computer readable medium including instructions that, when executed by at least one hardware processor of a computing system, cause the computing system to perform a method comprising: obtaining a comment submitted by a user on a page associated with an entity; determining a training data set, including a plurality of comments, that indicates whether each of the plurality of comments is actionable; training a machine learning model based on the training data set; and determining whether the comment is actionable based at least in part on the machine learning model. 17. The non-transitory computer readable medium of claim 16, wherein the determining whether the comment is actionable comprises associating the comment with a first classification in response to determining that the comment is actionable, the first classification indicative of a comment being actionable. 18. The non-transitory computer readable medium of claim 17, wherein the machine learning model provides the first classification and a confidence score associated with the first classification. 19. The non-transitory computer readable medium of claim 18, wherein the first classification is associated with the comment when the confidence score associated with the first classification satisfies a threshold value. 20. The non-transitory computer readable medium of claim 18, wherein the method further comprises displaying the first classification in a user interface associated with the page when the confidence score associated with the first classification satisfies a threshold value.