Abstract: Aspects of the present disclosure identify social media conversational signals and deliver prospects of potential opportunities to conduct a sale in an automated fashion. Individuals, or groups of people, are identified who are in decision making mode, and the communications are presented to businesses and/or organizations to help complete the transaction. Unlike social listening platforms, which use keyword matching and sentiment analysis, in some embodiments this platform leverages machine learning (ML), natural language processing (NLP) and the Universal Human Relevance System (UHRS) to identity relevant results by classifying them into a domain specific taxonomy. These transactional events may be defined by the date and time stamp, what the potential customer is looking for, the time-frame for consideration of the purchase, and the geographic location of the individual at the time of the signal's publication. In addition, these transactional events can be customized to suit the context of a domain.

Abstract: Example methods, apparatuses, and systems (e.g., machines) are presented for a natural language classification engine or platform capable of processing configurable classification criteria in real time or near real time. While typical classification engines tend to require specific training for each domain to be classified for a subscriber, the classification engine of the present disclosure is capable of analyzing a single corpus of human communications and providing only the relevant messages or documents according to criteria generated on the fly by a subscriber. The classification engine of the present disclosure need not know beforehand what type of content is desired by the subscriber. In this way, the criteria specified by a subscriber can change dynamically, and the classification engine of the present disclosure may be capable of evaluating the criteria and then provide relevant documents or messages according to the changed criteria, without needing additional corpus training.

Abstract: Example methods, apparatuses, and systems (e.g., machines) are presented for an auction platform that determines winning bids for high value digital message content using a multi-tiered share-rank system. The auction platform allows for subscribers to bid for merely a share of a set of high value digital message content. Multiple other subscribers may also bid for the same share amount of the set of high value digital message content. The collective bids of multiple subscribers that equals the entire set of the high value digital message content is then compared against other subscribers who have formed collective bids by different sized shares.

Abstract: Examples are presented for a classification system that utilizes multiple classification models to adapt to any desired set of raw data to be classified. The classification system may include multiple classification models stored in a model repository. A truth set of the raw data may be used to evaluate the fitness of each of the stored classification models. The models may be scored and ranked to determine which is the most appropriate to use for real time classification of the raw data. The optimal classification model may be used in a classification engine to classify the raw data in real time. This generates a classified output that may be interacted with by a user. A user interface may be used to permit feedback of the classified output to be generated. This feedback may then be transmitted to the offline system and recorded to further improve the classification models.

Abstract: A system and method for digitally classifying and analyzing exposure to behavioral influencers to probabilistically determine behaviors likely to be demonstrated by an individual or cohorts of individuals based on a combination of demographic and psychographic attributes. The system and method transforms raw data into useful data elements having associated exteroceptive values and other metadata that is useful for further evaluation, analysis, integration into a model, or other behavioral data utilization.

Abstract: A system and method for digitally classifying and analyzing exposure to behavioral influencers to probabilistically determine behaviors likely to be demonstrated by an individual or cohorts of individuals based on a combination of demographic and psychographic attributes. The system and method transforms raw data into useful data elements having associated exteroceptive values and other metadata that is useful for further evaluation, analysis, integration into a model, or other behavioral data utilization.

Abstract: A system and method for digitally classifying and analyzing exposure to behavioral influencers to probabilistically determine behaviors likely to be demonstrated by an individual or cohorts of individuals based on a combination of demographic and psychographic attributes. The system and method transforms raw data into useful data elements having associated exteroceptive values and other metadata that is useful for further evaluation, analysis, integration into a model, or other behavioral data utilization.