Abstract: A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Abstract: A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Abstract: A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Abstract: A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Abstract: Embodiments of a search system that provides knowledge based searching of content based on a knowledge model created from the content being searched are disclosed. Embodiments of such search systems may build a model of entitles and relationships representing the collective knowledge contained in a set of content analyzed. When a search is performed on content the model may be leveraged to improve the accuracy, relevance and recall of the search.

Abstract: A social analytic system may identify the social signals associated with a brand, campaign, or any other topic. The social analytic system may generate a vocabulary associated with the brand, campaign, or topic based terms used in the associated social signals. The vocabulary may be used for generating social media analytics and identifying social media events, such as marketing campaigns. In one example, a vocabulary may be compared with vocabularies associated with different constituents to identify the positive and negative terms in the vocabulary.

Abstract: A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Abstract: Embodiments of a search system that provides knowledge based searching of content based on a knowledge model created from the content being searched are disclosed. Embodiments of such search systems may build a model of entitles and relationships representing the collective knowledge contained in a set of content analyzed. When a search is performed on content the model may be leveraged to improve the accuracy, relevance and recall of the search.

Abstract: A social analytic system may collect social signals from different social network accounts. The social signals may be associated with different ecosystems. Time series data may be generated from the social signals and the time series data may be filtered to remove at least some generic or unrelated trends. Different data sets from the time series data may be associated with different ecosystem metrics. The social analytic system may compare different filtered time series data sets to identify different ecosystem events. For example, the comparisons may be used to identify highly correlated ecosystem metrics and ecosystem anomalies, and predict ecosystem events.

Abstract: A model based social analytic system collects social signals from social network accounts for different companies and constituents. The social signals can be used to benchmark social network performance for different contextual dimensions, such as industries, companies, brands, etc. Conversations may be identified in the signals and the types of constituents participating in the conversations may be identified. Analytics can then be determined for the contextual dimensions based on the related conversations and the types of constituents participating in the conversations.