Narratives have served as the basis of communication for thousands of years. Human beings have evolved with the essential ability to capture information and pass it on to others in the form of stories, songs, traditions, news, etc.
Humans may be naturally wired for storytelling and narrative communication, but computers are not. Your PC or phone may be able to look up the definitions of the word “dust” as a noun or verb, but the distinction of nuance in the phrase “she dusted my cake with powdered sugar” might be lost.
Recent advances in machine learning and semantic analysis, along with the availability of significant sources of online data, have made it possible for computer software to more deeply parse and provide rich context for narratives, enabling new tools for analyzing and influencing human behavior. Narratives can be ingested, analyzed and linked on a large scale, enabling “big data” techniques to be employed. Unexpected and unconstrained results will be possible by further analysis using machine learning and other techniques.
Examples of narratives include:
- Family and organizational histories, stories and traditions
- Legends, stories, and histories that underlie common beliefs of (for example) a nation or an organization
- Medical histories of a patient (e.g. from the doctor’s perspective, the patient’s perspective and/or a family member’s perspective)
- Knowledge possessed and passed on by subject matter experts within a corporation or university
- Online information including Facebook posts, news articles and blog entries
Deep Narrative Analysis parses and encodes these narratives into sets of knowledge graphs. It then combines the encoded narratives with broad contextual information from web-based and other sources and provides sophisticated analysis using machine learning and semantic technologies, yielding actionable insights.