Consumer insights analysis using word embeddings

1. A method comprising:

by a computing device in an online social network, receiving a request to generate a visualization of public sentiments regarding a particular subject by a plurality of clusters, wherein each cluster comprises a plurality of words semantically close to each other, wherein the request comprises one or more conditions characterizing a group of users, and wherein the request comprises an input n-gram representing the particular subject;

by the computing device, identifying users of the online social network who satisfy the one or more conditions;

by the computing device, constructing a first corpus of text by collecting text containing the input n-gram from a plurality of content objects in the online social network created by the identified users;

by the computing device, identifying a list of unique n-grams appearing in the first corpus of text;

by the computing device, generating, using a word embedding model, a table comprising unique n-grams in the list and their corresponding word vectors, wherein the word embedding model was trained using a second corpus of text collected from a plurality of user-created content objects in the online social network as training data, wherein each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space;

by the computing device, classifying word vectors in the table into a plurality of clusters based on semantic similarities of the word vectors;

by the computing device, calculating, for each of the plurality of clusters, a Term Frequency-Inverse Document Frequency (TF-IDF) score associated with the cluster;

by the computing device, determining k most relevant clusters to the particular subject based on the calculated TF-IDF scores; and

by the computing device, sending, as a response to the request, instructions to display n-grams associated with the determined k clusters in a two-dimensional display space, wherein n-grams corresponding to word vectors that belong to a cluster are displayed together.

2. The method of claim 1 , wherein the word embedding model is a word2vec model.

3. The method of claim 1 , wherein the classifying word vectors comprises performing a hierarchical clustering on the word vectors in the table.

4. The method of claim 1 , further comprises determining, for each n-gram in the table, a Term Frequency-Inverse Document Frequency (TF-IDF) score.

5. The method of claim 4 , wherein the instructions comprise instructions to adjust a font size for an n-gram based at least on a respective TF-IDF score assigned to the n-gram.

6. The method of claim 4 , wherein calculating a TF-IDF score associated with a cluster comprises taking an average of determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster.

7. The method of claim 4 , wherein calculating a TF-IDF score associated with a cluster comprises taking a maximum TD-IDF score among the determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster.

8. The method of claim 1 , wherein the instructions comprise instructions to assign a font color for n-grams in a semantic cluster.

9. The method of claim 1 , wherein

the second corpus of text is constructed by collecting texts from a plurality of content objects in the online social network created by the group of users that satisfy the one or more conditions.

10. The method of claim 9 , wherein the content objects were created within a pre-determined period of time.

11. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

receive a request to generate a visualization of public sentiments regarding a particular subject by a plurality of clusters, wherein each cluster comprises a plurality of words semantically close to each other, wherein the request comprises one or more conditions characterizing a group of users, and wherein the request comprises an input n-gram representing the particular subject;

identify users of the online social network who satisfy the one or more conditions;

construct a first corpus of text by collecting text containing the input n-gram from a plurality of content objects in an online social network created by the identified users;

identify a list of unique n-grams appearing in the first corpus of text;

generate, using a word embedding model, a table comprising unique n-grams in the list and their corresponding word vectors, wherein the word embedding model was trained using a second corpus of text collected from a plurality of user-created content objects in the online social network as training data, wherein each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space;

classify word vectors in the table into a plurality of clusters based on semantic similarities of the word vectors;

calculate, for each of the plurality of clusters, a Term Frequency-Inverse Document Frequency (TF-IDF) score associated with the cluster;

determine k most relevant clusters to the particular subject based on the calculated TF-IDF scores; and

send, as a response to the request, instructions to display n-grams associated with the determined k clusters in a two-dimensional display space, wherein n-grams corresponding to word vectors that belong to a cluster are displayed together.

12. The media of claim 11 , wherein the word embedding model is a word2vec model.

13. The media of claim 11 , wherein the classifying word vectors comprises performing a hierarchical clustering on the word vectors in the table.

14. The media of claim 11 , wherein the software is further operable when executed to determine, for each n-gram in the table, a Term Frequency-Inverse Document Frequency (TF-IDF) score.

15. The media of claim 14 , wherein the instructions comprise instructions to adjust a font size for an n-gram based at least on a respective TF-IDF score assigned to the n-gram.

16. The media of claim 11 , wherein the instructions comprise instructions to assign a font color for n-grams in a semantic cluster.

17. A system comprising:

one or more processors; and

one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:

receive a request to generate a visualization of public sentiments regarding a particular subject by a plurality of clusters, wherein each cluster comprises a plurality of words semantically close to each other, wherein the request comprises one or more conditions characterizing a group of users, and wherein the request comprises an input n-gram representing the particular subject;

identify users of the online social network who satisfy the one or more conditions;

construct a first corpus of text by collecting text containing the input n-gram from a plurality of content objects in an online social network created by the identified users;

identify a list of unique n-grams appearing in the first corpus of text;

generate, using a word embedding model, a table comprising unique n-grams in the list and their corresponding word vectors, wherein the word embedding model was trained using a second corpus of text collected from a plurality of user-created content objects in the online social network as training data, wherein each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space;

classify word vectors in the table into a plurality of clusters based on semantic similarities of the word vectors;

calculate, for each of the plurality of clusters, a Term Frequency-Inverse Document Frequency (TF-IDF) score associated with the cluster;

determine k most relevant clusters to the particular subject based on the calculated TF-IDF scores; and

send, as a response to the request, instructions to display n-grams associated with the determined k clusters in a two-dimensional display space, wherein n-grams corresponding to word vectors that belong to a cluster are displayed together.