Method and system for implementing semantic analysis of internal social network content

• US 9,727,925 B2
• Filed: 09/09/2013
• Issued: 08/08/2017
• Est. Priority Date: 09/09/2012
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Subramanian, Srividhya, Horton, Noah, Bear, Mary E. G., Setayesh, Mehrshad
• Primary Examiner(s): Moser, Bruce
• Application Number:
  US14/021,798
• Publication Number:
  US 20140074844A1
• Time in Patent Office: 1,429 Days
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/306 User profiles

Abstract

Disclosed is a method, system, and computer program product for semantically analyzing the content within an internal social network. Using the results of the analysis, the executives can gain a better understanding of, and insight into, the organization and its employees. A dashboard tool may be used in some embodiments of the invention to visualize the results of the semantic analysis.

Claims

1. A method implemented with a processor, comprising:

identifying an internal social network for an enterprise;

collecting a set of messages from the internal social network;

performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise;

performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages;

identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points;

as a result of the performed semantic analysis, clustering together messages that are similar to each other;

categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages;

associating each category of the plurality of categories with one or more tags;

associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages;

determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations; and

storing the tagged messages in an actionable social message store, the actionable message store storing in a table format, for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSA), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word.

2. The method of claim 1, further comprising displaying results of the semantic analysis on a dashboard tool.

3. The method of claim 1, in which results from performing the semantic analysis is used to gain insight into an organization or its employees.

4. The method of claim 1, in which the collected set of messages comprises business objects and conversations associated with the business objects.

5. The method of claim 1, in which the semantic analysis is performed in conjunction with filtering.

6. The method of claim 1, in which the semantic analysis comprises latent semantic analysis (LSA).

7. The method of claim 1, in which the semantic analysis identifies themes within the collected set of messages.

8. The method of claim 1, further comprising utilizing employee profile data to further analyze the collected set of messages.

9. The method of claim 1, wherein the one or more enterprise applications is a Human resources (“

HR”

) application, customer relations management (“

CRM”

) application, enterprise resource planning (“

ERP”

) application, or supply chain management application.

10. The method of claim 1, further comprising utilizing results of the semantic analysis to tailor communications to employees.

11. The method of claim 1, further comprising utilizing results of the semantic analysis to make budget allocations, spot bonuses, or make other monetary decisions.

12. The method of claim 1, in which the semantic analysis identifies topics discussed in the collected set of messages within the internal social network.

13. The method of claim 1, in which the semantic analysis is used to identify employee sentiments.

14. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process comprising:

identifying an internal social network for an enterprise;

collecting a set of messages from the internal social network;

performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise;

performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages;

identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points;

as a result of the performed semantic analysis, clustering together messages that are similar to each other;

categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages;

associating each category of the plurality of categories with one or more tags;

associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages;

determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations; and

storing the tagged messages in an actionable social message store, the actionable message store storing in a table format for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSAT), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word.

15. The computer readable medium of claim 14, wherein the sequence of instructions further comprises steps to display results of the semantic analysis on a dashboard tool.

16. The computer readable medium of claim 14, in which the collected set of messages comprises business objects and conversations associated with the business objects.

17. The computer readable medium of claim 14, in which the semantic analysis is performed in conjunction with filtering.

18. The computer readable medium of claim 14, in which the semantic analysis comprises latent semantic analysis (LSA).

19. The computer readable medium of claim 14, in which the semantic analysis identifies themes within the collected set of messages.

20. The computer readable medium of claim 14, wherein the sequence of instructions further comprises steps to utilize employee profile data to further analyze the collected set of messages.

21. The computer readable medium of claim 14, wherein the sequence of instructions further comprises steps to utilize results of the semantic to tailor communications to employees.

22. The computer readable medium of claim 14, wherein the sequence of instructions further comprises steps to utilize results of the semantic analysis to make budget allocations, spot bonuses, or make other monetary decisions.

23. The computer readable medium of claim 14, in which the semantic analysis identifies topics discussed in the collected set of messages within the internal social network.

24. The computer readable medium of claim 14, wherein the sequence of instructions further comprises steps to utilize results of the semantic analysis to identify employee sentiments.

25. A system, comprising:

a processor;

a memory comprising computer code executed using the processor, in which the computer code implements a process for, identifying an internal social network for an enterprise, collecting a set of messages from the internal social network, performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise, performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages, identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points, as a result of the performed semantic analysis, clustering together messages that are similar to each other, categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages, associating each category of the plurality of categories with one or more tags, associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages, determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations, and storing the tagged messages in an actionable social message store, the actionable message store storing in a table format, for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSA), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word.

26. The system of claim 25, further comprising a dashboard tool to visualize results from performing the semantic analysis.

27. The system of claim 25, in which the collected set of messages comprises business objects and conversations associated with the business objects.

28. The system of claim 25, in which the semantic analysis is performed in conjunction with filtering.

29. The system of claim 25, in which the semantic analysis comprises latent semantic analysis (LSA).

30. The system of claim 25, in which employee profile data is used to analyze the collected set of messages.

31. The system of claim 25, in which analysis results are used by company management to tailor communications to employees.

32. The system of claim 25, in which analysis results are used by company management to make budget allocations, spot bonuses, or make other monetary decisions.

33. The system of claim 25, in which the semantic analysis identifies topics discussed in the collected set of messages within the internal social network.

34. The system of claim 25, in which the semantic analysis is used to identify employee sentiments.