Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: One or more computing devices may access, from a set of profile objects each associated with an entity profile, a set of training objects each associated with an object-classification. The devices may identify, by an object-classifier algorithm, from a first set of content objects associated with the set of training objects, a first set of features each having a feature score indicating a correlation value with the object-classification. Based on the first set of features the devices may update the object-classifier algorithm and classify, by the updated object-classifier algorithm, one or more of the profile objects as being associated with the object-classification. The devices may identify, by the updated object-classifier algorithm, from a second set of content objects associated with the classified profile objects, a second set of features having feature scores greater than a threshold feature score, and update the object-classifier algorithm based on the second set of features.

Abstract: In one embodiment, a method includes accessing a set of training objects associated with an object-classification, identifying, from comments associated with the training objects, by an initial object-classifier algorithm configured to classify objects as associated with an object-classification by comparing comments associated with each object to one or more features, a first set of features, each feature having a corresponding text expression and a feature score indicating a correlation value between the feature and the object-classification, adding the first set of features to the algorithm to generate a revised object-classifier algorithm, accessing a set of test objects, classifying one or more of the test objects as associated with the object-classification, identifying, from comments associated with the classified test objects, a second set of features having feature scores greater than a threshold feature score, and adding the second set of features to the revised algorithm to generate a final object-

Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: In one embodiment, a method includes accessing a set of training objects associated with an object-classification, identifying, from comments associated with the training objects, by an initial object-classifier algorithm configured to classify objects as associated with an object-classification by comparing comments associated with each object to one or more features, a first set of features, each feature having a corresponding text expression and a feature score indicating a correlation value between the feature and the object-classification, adding the first set of features to the algorithm to generate a revised object-classifier algorithm, accessing a set of test objects, classifying one or more of the test objects as associated with the object-classification, identifying, from comments associated with the classified test objects, a second set of features having feature scores greater than a threshold feature score, and adding the second set of features to the revised algorithm to generate a final object-

Abstract: In one embodiment, a method includes accessing a first set of objects associated with an online social network, each object being associated with one or more comments. The method also includes generating a second set of objects from the first set of objects by applying a first filtering criteria to the first set of objects and scoring each object in the second set of objects based on the comments associated with each object. The method further includes generating a training set of objects from the second set of objects by selecting each object from the second set of objects having a score greater than a first threshold score, each object in the training set being associated with a first object-classification. The method further includes determining an object-classifier algorithm for the first object-classification, the object-classifier algorithm being determined through an iterative training process performed one or more times.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, n-grams from the text of the communication; identifying mention-terms from the extracted n-grams, each mention-term being a noun-phrase; calculating a term-score for each mention-term based on a frequency of occurrence of the mention-term in the communications; and generating a mentions-module including mentions, each mention including a mention-term having a term-score greater than a threshold term-score and text from communications comprising the mention-term.

Abstract: A gaming system includes electronic components that implement a value-aggregation procedure for displaying, combining, and awarding credit values in a reel-spinning venue.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a request associated with a particular content item; identifying communications authored by one or more users, each identified communication being associated with the particular content item; generating one or more search-results modules related to the particular content item, each search-results module being of a particular module type, wherein each search-results module includes information from a subset of the identified communications, the information corresponding to the particular module type of the search-results module, and wherein a number of communications in the subset of the identified communications including each search-results module is greater than a module-specific threshold number of communications; and sending, to the client system, a search-results interface comprising one or more of the search-results modules.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, quotations from the text of the communication; determining, for each extracted quotation, partitions of the quotation; grouping the extracted quotations into clusters based on a respective degree of similarity among their respective partitions; calculating a cluster-score for each cluster based on a frequency of occurrence of partitions of quotations in the cluster in the communications; and generating a quotations-module comprising representative quotations, each representative quotation being a quotation from a cluster having a cluster-score greater than a threshold cluster-score.

Abstract: In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, quotations from the text of the communication; determining, for each extracted quotation, partitions of the quotation; grouping the extracted quotations into clusters based on a respective degree of similarity among their respective partitions; calculating a cluster-score for each cluster based on a frequency of occurrence of partitions of quotations in the cluster in the communications; and generating a quotations-module comprising representative quotations, each representative quotation being a quotation from a cluster having a cluster-score greater than a threshold cluster-score.

Abstract: In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, n-grams from the text of the communication; identifying mention-terms from the extracted n-grams, each mention-term being a noun-phrase; calculating a term-score for each mention-term based on a frequency of occurrence of the mention-term in the communications; and generating a mentions-module including mentions, each mention including a mention-term having a term-score greater than a threshold term-score and text from communications comprising the mention-term.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a request associated with a particular content item; identifying communications authored by one or more users, each identified communication being associated with the particular content item; generating one or more search-results modules related to the particular content item, each search-results module being of a particular module type, wherein each search-results module includes information from a subset of the identified communications, the information corresponding to the particular module type of the search-results module, and wherein a number of communications in the subset of the identified communications including each search-results module is greater than a module-specific threshold number of communications; and sending, to the client system, a search-results interface comprising one or more of the search-results modules.

Abstract: In one embodiment, a method includes accessing a first set of objects associated with an online social network, each object being associated with one or more comments. The method also includes generating a second set of objects from the first set of objects by applying a first filtering criteria to the first set of objects and scoring each object in the second set of objects based on the comments associated with each object. The method further includes generating a training set of objects from the second set of objects by selecting each object from the second set of objects having a score greater than a first threshold score, each object in the training set being associated with a first object-classification. The method further includes determining an object-classifier algorithm for the first object-classification, the object-classifier algorithm being determined through an iterative training process performed one or more times.