Facebook, Inc.

Bryan Kauder

Jonathan Michael Arfa

Nikhil Girish Nawathe

Shriram Subramanian

Helen Tamara Crossley

Fang Xia

Ashim Datta

Alyx Catherine Stevens

Daniel Frenkel

In one embodiment, a method includes receiving a request to identify a similarity in public sentiments for each pair from a plurality of entities from a second computing device, where the request includes names of the plurality of entities, accessing a table of word vector relationships, where the table of word vector relationships includes a plurality of unique n-grams and their corresponding word vectors, and where each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space, looking up word vectors corresponding to each of the names using the table, calculating, for each of the word vectors, a similarity metric to each of the word vectors, and sending a response message to the second computing device, where the response message includes calculated similarity metrics corresponding to all the pairs of the word vectors.

Consumer insights analysis by identifying a similarity in public sentiments for a pair of entities

In one embodiment, a method includes receiving a request to identify a word representing a target concept that is in a first relationship with a particular concept such that the first relationship is analogous to a second relationship in which a first reference concept is with a second reference concept, accessing a table of word vector relationships, looking up a particular word vector, a first reference word vector, and a second reference word vector, determining an imaginary vector such that a first vector from the first reference word vector to the second reference word vector is equal to a second vector from the particular word vector to the imaginary vector, selecting a target word vector closest to the imaginary vector, identifying a target n-gram corresponding to the target word vector, and sending a response message comprising the target n-gram.

Consumer insights analysis using word embeddings

In one embodiment, a method includes receiving a request to generate k keywords each of which is semantically related to a particular subject, where the request includes an input n-gram representing the particular subject, accessing a table of word vector relationships, where the table includes a plurality of unique n-grams and their corresponding word vectors, and wherein each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space, looking up, using the table, a first word vector corresponding to the input n-gram, selecting k word vectors closest to the first word vector in the embedding space using the table and based on a similarity metric, identifying, for each of the selected word vectors, a corresponding n-gram by looking up the selected word vector in the table, and sending a response message including the identified n-grams.

In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.

In one embodiment, a method includes receiving a request to identify public sentiments for one or more entities from a second computing device, where each of the attribute n-grams represents an attribute among a plurality of pre-determined attributes, accessing a table of word vector relationships, looking up entity word vectors corresponding to each of the names for the one or more entities and attribute word vectors corresponding to each of the plurality of attribute n-grams using the table, calculating, for each of the entity word vectors, a similarity metric to each of the attribute word vectors, and sending a response message to the second computing device, where the response message includes calculated similarity metrics corresponding to all the pairs of an entity word vector and an attribute word vector.

Consumer insights analysis using entity and attribute word embeddings

In one embodiment, a method includes receiving a request to generate a visualization of public sentiments regarding a particular subject by a plurality of clusters, where each cluster includes a plurality of words semantically close to each other, constructing a first corpus of text by collecting text containing the input n-gram from a plurality of user-created content objects in the online social network, identifying a list of unique n-grams appearing in the first corpus of text, generating a table comprising unique n-grams in the list and their corresponding word vectors using a word embedding model, classifying word vectors in the table into a plurality of clusters based on semantic similarities of the word vectors, and sending, as a response to the request, instructions to display n-grams in the table in a two-dimensional display space, where n-grams corresponding to word vectors that belong to a cluster are displayed together.

In one embodiment, a method includes receiving a request to generate k words that each approximates a representation of a relationship between two concepts, where the request includes two input n-grams that each represent one of the two concepts, accessing a table of word vector relationships, where the table includes a plurality of unique n-grams and their corresponding word vectors, looking up word vectors corresponding to each of the two input n-grams using the table, calculating an average vector of the word vectors corresponding to the two input n-grams, selecting, using the table and based on a similarity metric, k word vectors closest to the average vector in the embedding space, identifying, for each of the selected word vectors, a corresponding n-gram by looking up the selected word vector in the table, and sending a response message, the response message comprising the identified n-grams.

In one embodiment, a method includes receiving a request to generate a two-dimensional visualization of public sentiments regarding a particular subject, where the request includes an input n-gram representing the particular subject, constructing a first corpus of text by collecting text containing the input n-gram from a plurality of user-created content objects in the online social network, identifying a list of unique n-grams appearing in the first corpus of text, generating a table comprising unique n-grams in the list and their corresponding word vectors using a word embedding model, condensing the d-dimensional word vectors in the table into a two-dimensional word vectors; and sending, as a response to the request, instructions to display n-grams in the table on a two-dimensional display space, where each n-gram is placed at a location of the corresponding condensed word vector.

A method includes accessing a first set of content objects of an online social network posted within a predetermined timeframe, and identifying trending topics. Each object may be associated with one or more topics. The method further includes accessing a second set of content objects, and identifying a plurality of co-occurring topics associated with the second set. Each content object of the second set is associated with at least one of the identified trending topics. Then, the method includes accessing a third set of content objects, and analyzing the plurality of trending topics and co-occurring topics to detect key topics. Each content object of the third set of content objects may be associated with at least one of the identified co-occurring topics or trending topics. The key topics may be detected based on determining the co-occurring topics or trending topics that are associated with a threshold number of content objects.

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New York, NY, United States of America

Bryan Kauder