Co-occurrence network

<p>Co-occurrence network, sometimes referred to as a <a href="/facts/Semantic_network/mt7FlP53">semantic network</a>, is a method to analyze text that includes a graphic <a href="/facts/Graph_visualization/uhh73BVo">visualization</a> of potential <a href="/facts/Ontology_components/EIRll6sn">relationships</a> <a href="/facts/Social_relationship/DU3HW2Ah">between people</a>, organizations, concepts, biological organisms like bacteria or other entities represented within written material. The generation and visualization of <a href="/facts/Co-occurrence/DgBC9aow">co-occurrence</a> networks has become practical with the advent of electronically stored text compliant to <a href="/facts/Text_mining/5QsIa3Cz">text mining</a>.
</p><p>By way of definition, co-occurrence networks are the collective <a href="/facts/Interconnection/3BMeTFXp">interconnection</a> of terms based on their paired presence within a specified unit of text. Networks are generated by connecting pairs of terms using a set of criteria defining co-occurrence. For example, terms A and B may be said to “co-occur” if they both appear in a particular article. Another article may contain terms B and C. Linking A to B and B to C creates a co-occurrence network of these three terms. Rules to define co-occurrence within a <a href="/facts/Text_corpus/bYeEQQdh">text corpus</a> can be set according to desired criteria. For example, a more stringent criteria for co-occurrence may require a pair of terms to appear in the same <a href="/facts/Sentence_(linguistics)/GhKKkcm2">sentence</a>. Co-occurrence networks were found to be particularly useful to analyze large text and <a href="/facts/Big_data/XGtrVg4z">big data</a>, 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.
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Co-occurrence network open-in-new

Co-occurrence network