A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: identifying text content of a page of a social networking system; extracting a topic from the text content; receiving information about a first interaction between a user of the social networking system and the page; determining a sentiment of the user toward the text content based on the received information about the first interaction; inferring by a computer system that the user has a sentiment polarity toward the extracted topic based on the received information about the first interaction, the sentiment polarity corresponding to the user's sentiment toward the text content and indicating a positive or negative sentiment of the user toward the topic; storing the inferred sentiment polarity of the user toward the extracted topic; receiving a second interaction from the user related to the extracted topic; retrieving the user's stored sentiment polarity toward the extracted topic; determining that the second interaction has the same stored sentiment polarity of the user toward the extracted topic based on the second interaction being from the same user as the first interaction; and storing, in connection with the second interaction, the determined sentiment polarity of the user toward the extracted topic. 2. The method of claim 1, wherein the text content comprises at least one of posts or comments. 3. The method of claim 1, wherein the topic is extracted from the text content using lexicon-based analysis. 4. The method of claim 1, wherein the received information further comprises information about a separate interaction between another user with whom the user has established a connection in the social networking system and the page, and the sentiment polarity of the user is further inferred based on the information about the separate interaction. 5. The method of claim 1, further comprising: receiving information about a separate interaction between another user in the social networking system and the interaction; and inferring the sentiment polarity of the user based on the information about the separate interaction. 6. The method of claim 1, further comprising: receiving information about a separate interaction between the user and another interaction between another user in the social networking system and the page; and inferring the sentiment polarity of the user based on the information about the separate interaction. 7. The method of claim 1, further comprising: adding the sentiment polarity to an insight page, the insight page comprising statistical analytics on the page and providing insight for predicting future trends based on the statistical analytics. 8. The method of claim 7, further comprising: identifying a consistent trend of the user's sentiment polarity toward the content of the page based on the insight page; determining whether the inferred sentiment polarity follows the consistent trend; and responsive to determining that the inferred sentiment polarity does not follow the consistent trend, identifying the inferred sentiment polarity as an aberrant entry. 9. The method of claim 8, further comprising: responsive to determining that the inferred sentiment polarity follows the consistent trend, identifying the inferred sentiment polarity as a correct entry. 10. The method of claim 7, further comprising: identifying a consistent trend of the user's sentiment polarity toward the content of the page based on the insight page; and determining that the user has switched between a person displaying a negative sentiment polarity toward the content of the page and a person displaying a positive sentiment polarity toward the content of the page based on the consistent trend. 11. The method of claim 1, wherein inferring by a computer system that the user has a sentiment polarity toward the extracted topic based on the received information about the first interaction comprises: determining a type of the first interaction from a plurality of different types of interactions; and inferring the sentiment polarity based on the determined type of the first interaction. 12. A computer-implemented method comprising: identifying content of a page of a social networking system; receiving information about a first interaction between a user of the social networking system and the page; inferring by a computer system a sentiment polarity of the user based on the received information about the first interaction, the sentiment polarity indicating a positive or negative sentiment of the user toward the content of the page; storing the inferred sentiment polarity of the user toward the content of the page; receiving a second interaction from the user related to the content; retrieving the user's stored sentiment polarity toward the content of the page; determining that the second interaction has the same stored sentiment polarity of the user toward the content of the page based on the second interaction being from the same user as the first interaction; and storing, in connection with the second interaction, the determined sentiment polarity of the user toward the content of the page. 13. The method of claim 12, wherein the first interaction comprises at least one of liking, crossing-out, sharing, hiding, reporting, or commenting. 14. The method of claim 12, further comprising: disqualifying sponsored stories related to the content of the page when the sentiment polarity of the user falls below a threshold. 15. The method of claim 12, wherein the step of inferring the sentiment polarity comprises: identifying users of the social networking system possessing a strong sentiment polarity toward the content as a set of trusted users associated with the content; determining a data set associated with each trusted user, the data set comprising information about interactions between said each trusted user and the page in conjunction with said each trusted user's sentiment polarity; training a machine learning model using the determined data sets as a training set; and inferring the sentiment polarity of the user using the trained machine learning model based on the received information. 16. The method of claim 15, wherein the step of identifying the set of trusted users comprises: receiving a third interaction between a second user of the social networking system and the page, the third interaction comprising the second user liking the page; determining that the second user has a positive sentiment polarity toward the content of the page; and adding the second user to a set of positive trusted users associated with the content of the page, the set of positive trusted users forming a subset of the set of trusted users. 17. The method of claim 15, wherein the step of identifying the set of trusted users comprises: receiving an indication that a positive trusted user likes a comment on the page from an outside user, wherein the outside user does not belong to the set of trusted users and the comment has not been analyzed; analyzing the comment to obtain a sentiment polarity of the comment toward the content of the page; determining that the sentiment polarity of the comment is above a threshold sentiment polarity; determining that the outside user has a positive sentiment polarity toward the content of the page; and adding the outside user to a set of positive trusted users associated with the content of the page, the set of positive trusted users forming a subset of the set of trusted users. 18. The method of claim 15, wherein the step of identifying the set of trusted users comprises: identifying an outside user who likes a set of comments on the page, wherein the outside user does not belong to the set of trusted users; determining that the set of comments has more than a threshold number of comments; determining that more than a threshold percentage of the set of comments is from the set of positive trusted users; determining that the outside user has a positive sentiment polarity toward the content of the page; and adding the outside user to a set of positive trusted users associated with the content of the page, the set of positive trusted users forming a subset of the set of trusted users. 19. The method of claim 15, wherein the step of identifying the set of trusted users comprises: receiving a set of comments from an outside user on the page, wherein the outside user does not belong to the set of trusted users; analyzing each comment from the set of comments to obtain a sentiment polarity of said each comment toward the content of the page; determining that the set of comments comprises a subset of comments, wherein each comment from the subset of comments has a sentiment polarity below a threshold sentiment polarity; determining that the number of comments within the subset of comments exceeds a threshold number; determining that the outside user has a negative sentiment polarity toward the content of the page; and adding the outside user to a set of negative trusted users associated with the content of the page, the set of negative trusted users forming a subset of the set of trusted users. 20. The method of claim 17, wherein analyzing the comment comprises using lexicon-based analysis. 21. The method of claim 19, wherein each comment from the set of comments is analyzed using lexicon-based analysis. 22. The method of claim 15, wherein the training set is independent of language. 23. The method of claim 15, further comprising: associating each trusted user's comments related to the content of the page with said each trusted user's sentiment polarity; forming a separate training set for training a bag-of-words sentiment analyzer, the separate training set comprising a set of comments from the trusted users in conjunction with each comment's sentiment polarity. 24. The method of claim 12, wherein inferring by the computer system a sentiment polarity of the user based on the received information about the first interaction comprises: determining a type of the first interaction from a plurality of different types of interactions; and inferring the sentiment polarity based on the determined type of the first interaction. 25. A computer-implemented method comprising: identifying content of a page of a social networking system; receiving information about a first interaction between a non-trusted user of the social networking system and the page; inferring by a computer system a first sentiment polarity of the non-trusted user based on the received information about the first interaction, the sentiment polarity indicating a first sentiment of the non-trusted user toward the content of the page; storing the inferred first sentiment polarity of the non-trusted user toward the content of the page; receiving a second interaction from a trusted user related to the content indicating a second sentiment polarity toward the first interaction by the non-trusted user; retrieving the non-trusted user's stored first sentiment polarity toward the content of the page; determining the trusted user's second sentiment polarity toward the content of the page is different from the non-trusted user's first sentiment polarity toward the content of the page based on the second interaction from the trusted user; adjusting the non-trusted user's first sentiment polarity toward the content in a direction corresponding to the second sentiment polarity indicated by the trusted user responsive to determining that the first sentiment polarity is different from the second sentiment polarity; and storing, in connection with the second interaction, the adjusted first sentiment polarity of the non-trusted user toward the content of the page. 26. The method of claim 25, wherein inferring by the computer system a first sentiment polarity of the non-trusted user based on the received information about the first interaction comprises: determining a type of the first interaction from a plurality of different types of interactions; and inferring the sentiment polarity based on the determined type of the first interaction. 27. A computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: identifying text content of a page of a social networking system; extracting a topic from the text content; receiving information about a first interaction between a user of the social networking system and the page; determining a sentiment of the user toward the text content based on the received information about the first interaction; inferring by a computer system that the user has a sentiment polarity toward the extracted topic based on the received information about the first interaction, the sentiment polarity corresponding to the user's sentiment toward the text content and indicating a positive or negative sentiment of the user toward the topic; storing the inferred sentiment polarity of the user toward the extracted topic; receiving a second interaction from the user related to the extracted topic; retrieving the user's stored sentiment polarity for the extracted topic; determining that the second interaction has the same stored sentiment polarity of the user toward the extracted topic based on the second interaction being from the same user as the first interactions; and storing, in connection with the second interaction, the determined sentiment polarity of the user toward the extracted topic. 28. The computer program product of claim 27, wherein the computer program code for inferring by the computer system a sentiment polarity of the user based on the received information about the first interaction comprises computer program code for: determining a type of the first interaction from a plurality of different types of interactions; and inferring the sentiment polarity based on the determined type of the first interaction. 29. A computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: identifying content of a page of a social networking system; receiving information about a first interaction between a user of the social networking system and the page; inferring by a computer system a sentiment polarity of the user based on the received information about the first interaction, the sentiment polarity indicating a positive or negative sentiment of the user toward the content of the page; storing the inferred sentiment polarity of the user toward the content of the page; receiving a second interaction from the user related to the content; retrieving the user's stored sentiment polarity for the extracted topic; determining that the second interaction has the same stored sentiment polarity of the user toward the extracted topic based on the second interaction being from the same user as the first interactions; and storing, in connection with the second interaction, the determined sentiment polarity of the user toward the content of the page. 30. The computer program product of claim 29, wherein the computer program code for inferring by the computer system a sentiment polarity of the user based on the received information about the first interaction comprises computer program code for: determining a type of the first interaction from a plurality of different types of interactions; and inferring the sentiment polarity based on the determined type of the first interaction.