Co-occurrence network

Co-occurrence network, sometimes referred to as a semantic network,[1] is a method to analyze text that includes a graphic visualization of potential relationships between people, organizations, concepts, biological organisms like bacteria[2] or other entities represented within written material.

By way of definition, co-occurrence networks are the collective interconnection of terms based on their paired presence within a specified unit of text.

Co-occurrence networks were found to be particularly useful to analyze large text and big data, when identifying the main themes and topics (such as in a large number of social media posts), revealing biases in the text (such as biases in news coverage), or even mapping an entire research field.

Graphic representation of co-occurrence networks allow them to be visualized and inferences drawn regarding relationships between entities in the domain represented by the dictionary of terms applied to the text corpus.

PubGene is an example of an application that addresses the interests of biomedical community by presenting networks based on the co-occurrence of genetics related terms as these appear in MEDLINE records.

[12] The website NameBase is an example of how human relationships can be inferred by examining networks constructed from the co-occurrence of personal names in newspapers and other texts (as in Ozgur et al.[13]).