Purpose: We present an analytical, open source and flexible natural language processing and text mining method for topic evolution, emerging topic detection and research trend forecasting for all kinds of data-tagged text.
Design/methodology/approach: We make full use of the functions provided by the open source VOSviewer and Microsoft Office, including a thesaurus for data clean-up and a LOOKUP function for comparative analysis.
Findings: Through application and verification in the domain of perovskite solar cells research, this method proves to be effective.
Research limitations: A certain amount of manual data processing and a specific research domain background are required for better, more illustrative analysis results. Adequate time for analysis is also necessary.
Practical implications: We try to set up an easy, useful, and flexible interdisciplinary text analyzing procedure for researchers, especially those without solid computer programming skills or who cannot easily access complex software. This procedure can also serve as a wonderful example for teaching information literacy.
Originality/value: This text analysis approach has not been reported before.