Abstract: Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.

Abstract: Systems, methods, and media for developing ontologies and analyzing communication data are provided herein. In an example implementation, the method includes: identifying terms in in a set of communication data; identifying a list of possible relations of the identified terms; scoring the possible relations according to a set of predefined merits; ranking the possible relations into a list of possible relations in descending order according to their score; and tagging relations in the set of communication data. The relations may be tagged by identifying the possible relations in the communication data in order corresponding with the list of possible relations. The possible relations that have lower rankings that conflict with higher ranking relations are not tagged. The conflicts may be determined by a predefined set of conflict criteria.

Abstract: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: An embodiment of the method of processing communication data to identify one or more themes within the communication data includes identifying terms in a set of communication data, wherein a term is a word or short phrase, and defining relations in the set of communication data based on the terms, wherein the relation is a pair of terms that appear in proximity to one another. The method further includes identifying themes in the set of communication data based on the relations, wherein a theme is a group of one or more relations that have similar meanings, and storing the terms, the relations, and the themes in the database.

Abstract: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: Systems, methods, and media for developing ontologies and analyzing communication data are provided herein. In an example implementation, the method includes: identifying terms in in a set of communication data; identifying a list of possible relations of the identified terms; scoring the possible relations according to a set of predefined merits; ranking the possible relations into a list of possible relations in descending order according to their score; and tagging relations in the set of communication data. The relations may be tagged by identifying the possible relations in the communication data in order corresponding with the list of possible relations. The possible relations that have lower rankings that conflict with higher ranking relations are not tagged. The conflicts may be determined by a predefined set of conflict criteria.

Abstract: Improved systems and method as disclosed herein, provide automated analysis tools for more refined trend analysis and evaluation of identified trends. Communication data may be recognized as either audio or textual data which may be processed and analyzed in real-time (as in the case of streaming audio data) or processed at a time apart from the acquisition of the communication data. If the communication data is audio data, then the audio data, may undergo a transcription, which may employ the exemplary technique of large vocabulary continuous speech recognition (LVCSR) or other known speech-to-text algorithms or techniques. Alternatively, the communication data may already be in the form of a transcription or the communication data may have originated as textual data, exemplarily the communication data is from an internet web chat, email, text message, or social media.

Abstract: A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

Abstract: Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.