Automated systems and methods that generate affect-annotated timelines

An affect-annotation system that receives conversation data generated from a monitored conversation and processes the input conversation data to produce an electronic representation of the monitored conversation, the affect-annotation system comprising:

• one or more processors;
• one or more memories and mass-storage devices;
• and
• computer instructions, stored in one or more of the one or more memories and mass-storage devices that, when executed by one or more of the one or more processors, control the affect-annotation system to
• receive conversation data, including one or more of
• video data,
• audio data, and
• physiological data,
• generate multiple affect-annotation records for each of multiple conversation units identified using the received conversation data,
• each conversation unit corresponding to a subinterval of the monitored conversation, attributed to a particular source, generated, by the automated affect-annotation system, from one or more units of language for affect coding, referred to as “ULACs,” each ULAC corresponding to a minimal aggregation of words extracted from a conversation that conveys an intra-contextual meaning, and identified by the automated affect-annotation system based on lexical dependency graphs, and
• each affect-annotation record containing a textual representation of a conversation unit, an indication of a source of the conversation unit, an indication of a temporal duration of the conversation unit, an indication of a temporal position of the conversation unit within the monitored conversation, and an affect-code probability distribution,
• aggregate the generated affect-annotation records into an affect-annotated-timeline data structure, and
• output the affect-annotated-timeline data structure to one or more of a display device, a data-storage appliance or device, and one or more downstream analysis systems.