Method and system for implementing semantic analysis of internal social network content
First Claim
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1. A computer-implemented method, comprising:
- identifying one or more social networks for an enterprise;
collecting a set of messages from the one or more social networks;
performing semantic filtering on the set of messages from the one or more social networks, 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 one or more social networks to determine a contextual significance of one or more terms in the set of messages;
clustering together messages that are similar to each other as a result of the performed semantic analysis;
identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points;
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; and
generating actionable data for the one or more enterprise applications based at least in part on the determined associations.
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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.
56 Citations
36 Claims
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1. A computer-implemented method, comprising:
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identifying one or more social networks for an enterprise; collecting a set of messages from the one or more social networks; performing semantic filtering on the set of messages from the one or more social networks, 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 one or more social networks to determine a contextual significance of one or more terms in the set of messages; clustering together messages that are similar to each other as a result of the performed semantic analysis; identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points; 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; and generating actionable data for the one or more enterprise applications based at least in part on the determined associations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. 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:
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identifying one or more social networks for an enterprise; collecting a set of messages from the one or more social networks; performing semantic filtering on the set of messages from the one or more social networks, 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 one or more social networks to determine a contextual significance of one or more terms in the set of messages; clustering together messages that are similar to each other as a result of the performed semantic analysis; identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points; 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; and generating actionable data for the one or more enterprise applications based at least in part on the determined associations. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A system, comprising:
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a processor; and a memory comprising computer code executed using the processor, in which the computer code implements a process for, identifying one or more social networks for an enterprise, collecting a set of messages from the one or more social networks, performing semantic filtering on the set of messages from the one or more social networks, 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 one or more social networks to determine a contextual significance of one or more terms in the set of messages, clustering together messages that are similar to each other as a result of the performed semantic analysis, identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points, 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, and generating actionable data for the one or more enterprise applications based at least in part on the determined associations. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
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Specification