METHOD AND SYSTEM FOR CREATING FILTERS FOR SOCIAL DATA TOPIC CREATION

• US 20160092551A1
• Filed: 09/24/2015
• Published: 03/31/2016
• Est. Priority Date: 09/26/2014
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: SETAYESH, Mehrshad, MCCANDLESS, Timothy P., TANG, Glenn
• Granted Patent: US 10,146,878 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

Disclosed is a system, method, and computer program product for performing semantic analysis and creating topics with regards to social data. A user interface is provided that allows the user to view and interact with to view and control the process/mechanism or creating topics. The user interface allows the user to create one or more text-based filters and metadata filters based on which social data for each topic is filtered.

Claims

1. A method, comprising:

receiving a message associated with a social media communication, wherein the message comprises at least one metadata field and at least one content field;

retrieving a set of metadata filters and text-based filters for a plurality of topics, wherein a metadata filter of the set of metadata filters defines criteria that must be satisfied within the at least one metadata field in a metadata structure of the message, and wherein a text-based filter of the set of text-based filters defines criteria that must be satisfied within the at least one content field in a content structure of the message;

analyzing the message against the retrieved metadata and text-based filters, wherein the metadata structure of the message is analyzed against the metadata filters, and wherein the content structure of the message is analyzed against the text-based filters;

annotating the message with one or more identifiers associated with a topic based at least in part on whether the metadata filters and text-based filters for the topic are satisfied; and

categorizing the message for the topic based at least in part on both;

(i) whether the message comprises an annotation corresponding to the metadata filters associated with the topic, and

(ii) whether the message comprises an annotation corresponding to the text-based filters associated with the topic.

2. The method of claim 1, further comprising:

when the text-based filter is a topic vector, accessing a semantic space to vectorize the message, wherein the topic vector is a representation of semantic significance of one or more terms; and

analyzing the vectorized message against the topic vector, wherein the message is annotated with the one or more identifiers based at least in part on the analysis.

3. The method of claim 1, wherein the text-based filter comprises a keyword search.

4. The method of claim 1, wherein the text-based filter comprises a Boolean operator.

5. The method of claim 1, wherein the metadata filter pertains to a geographical location associated with a message of the set of messages.

6. The method of claim 1, wherein the metadata filter pertains to a content type of a message of the set of messages.

7. The method of claim 1, wherein the metadata filter pertains to a URL associated with a message of the set of messages.

8. The method of claim 1, wherein the metadata filter pertains to an author associated with a message of the set of messages.

9. The method of claim 1, wherein a particular annotation for the message specifies at least (i) whether the particular annotation is triggered based on any of the metadata filters, and (ii) the topic detected by the particular annotation.

10. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a set of acts to perform semantic analysis on a set of data, the set of acts comprising:

receiving a message associated with a social media communication, wherein the message comprises at least one metadata field and at least one content field;

retrieving a set of metadata filters and text-based filters for a plurality of topics, wherein a metadata filter of the set of metadata filters defines criteria that must be satisfied within the at least one metadata field in a metadata structure of the message, and wherein a text-based filter of the set of text-based filters defines criteria that must be satisfied within the at least one content field in a content structure of the message;

analyzing the message against the retrieved metadata and text-based filters, wherein the metadata structure of the message is analyzed against the metadata filters, and wherein the content structure of the message is analyzed against the text-based filters;

annotating the message with one or more identifiers associated with a topic based at least in part on whether the metadata filters and text-based filters for the topic are satisfied; and

categorizing the message for the topic based at least in part on both;

(i) whether the message comprises an annotation corresponding to the metadata filters associated with the topic, and

(ii) whether the message comprises an annotation corresponding to the text-based filters associated with the topic if the metadata filters and the text-based filters are satisfied.

11. The computer program product of claim 10, wherein the set of acts further comprise:

when the text-based filter is a topic vector, accessing a semantic space to vectorize the message, wherein the topic vector is a representation of semantic significance of one or more terms; and

analyzing the vectorized message against the topic vector, wherein the message is annotated with the one or more identifiers based at least in part on the analysis.

12. The computer program product of claim 10, wherein the text-based filter comprises a keyword search.

13. The computer program product of claim 10, wherein the text-based filter comprises a Boolean operator.

14. The computer program product of claim 10, wherein the metadata filter pertains to a geographical location associated with a message of the set of messages.

15. The computer program product of claim 10, wherein the metadata filter pertains to a content type of a message of the set of messages.

16. The computer program product of claim 10, wherein the metadata filter pertains to a URL associated with a message of the set of messages.

17. The computer program product of claim 10, wherein the metadata filter pertains to an author associated with a message of the set of messages.

18. The computer program product of claim 10, wherein a particular annotation for the message specifies at least (i) whether the particular annotation is triggered based on any of the metadata filters, and (ii) the topic detected by the particular annotation.

19. A computer system for performing semantic analysis, the computer system comprising:

a computer processor to execute a set of program code instructions; and

a memory to hold the program code instructions, in which the program code instructions comprises program code,

to receive a message associated with a social media communication, wherein the message comprises at least one metadata field and at least one content field;

to retrieve a set of metadata filters and text-based filters for a plurality of topics, wherein a metadata filter of the set of metadata filters defines criteria that must be satisfied within the at least one metadata field in a metadata structure of the message, and wherein a text-based filter of the set of text-based filters defines criteria that must be satisfied within the at least one content field in a content structure of the message;

to analyze the message against the retrieved metadata and text-based filters, wherein the metadata structure of the message is analyzed against the metadata filters, and wherein the content structure of the message is analyzed against the text-based filters;

to annotate the message with one or more identifiers associated with a topic based at least in part on whether the metadata filters and text-based filters for the topic are satisfied; and

to categorize the message for the topic based at least in part on both;

(i) whether the message comprises an annotation corresponding to the metadata filters associated with the topic, and

(ii) whether the message comprises an annotation corresponding to the text-based filters associated with the topic if the metadata filters and the text-based filters are satisfied

20. The system of claim 19, further comprising

when the text-based filter is a topic vector, to access a semantic space to vectorize the message, wherein the topic vector is a representation of semantic significance of one or more terms;

and

to analyze the vectorized message against the topic vector, wherein the message is annotated with the one or more identifiers based at least in part on the analysis.

21. The system of claim 19, wherein the text-based filter comprises a keyword search.

22. The system of claim 19, wherein the text-based filter comprises a Boolean operator.

23. The system of claim 19, wherein the metadata filter pertains to a geographical location associated with a message of the set of messages.

24. The system of claim 19, wherein the metadata filter pertains to a content type of a message of the set of messages.

25. The system of claim 19, wherein the metadata filter pertains to a URL associated with a message of the set of messages.

26. The system of claim 19, wherein the metadata filter pertains to an author associated with a message of the set of messages.

27. The system of claim 19, wherein a particular annotation for the message specifies at least (i) whether the particular annotation is triggered based on any of the metadata filters, and (ii) the topic detected by the particular annotation.