Complete Citation Analysis
Distribution Network Analysis
Word frequency analysis
Term Frequency analysis
Citation Network Analysis
We focus on relevance and the extent of interest derived in ‘mining’ your data. Text mining derives information from text and can reveal patterns and themes through systematic structuring. Text mining includes reproducing discernible categories of data to extract relational concepts.
One of the outputs of text mining is to produce granular taxonomies. This data mining can reveal information high quality information about a body of text that is seemingly lost when less systematic approaches are used.
Citation analysis provides links to citation metadata to identify influence. It is often of interest to find out who cites what and who cites whom. With citation analysis, frequencies and patterns are revealed when once document is linked to others.
Co-citation analysis, an analysis of the frequency that two documents are cited together, is one example and can provide meaningful associations between citing and citing authors and articles.
Bibliometric analysis examines all bibliometric information attached to a particular document such as books or journal articles. It’s widely used in library and information science and in scientometrics.
Its many uses include providing a citation graph to determine the extent of influence of a particular author or piece of work.
Social Network Analysis
With popular applications in the social media literature, SNA focuses on finding social structures that exist within networks. A popular SNA application is one or several users of Facebook and how they are connected with their ‘friends’. This network can determine close or remote connections, kinships and relationships.
SNA has been applied not only on finding social media structures but are now embedded in data visualisation softwares that allow the examination of knowledge flows in specific disciplines.
There are a number of platforms available to visualise large data (e.g. citations data) such as Gephi or VOSViewer or Kumu. These platforms often use algorithms to provide a detailed analysis of networks and community detection capabilities.
In essence, these visualisation tools make it easy to make sense of a large amount of data in a format that can be easily readable and usable for decision making or simply providing a dynamic display of how each node is connected to others in a network.