The LiTCoF (Literature Topic Co-occurrence and Frequency) tool is a Python-based tool to perform a literature review using text mining techniques from Data Science. Specifically, the LiTCoF tool measures and plots the frequency, co-occurrence, and evolution over time of user-defined keywords. Moreover, it uses Latent Dirichlet Allocation (LDA) to uncover the primary topics that emerge from the text it is given (i.e., topic modeling). The LiTCoF tool notably combines several impressive features from existing Python libraries, including Scikit Learn and Gensim. The LiTCoF was developed for the forum article "A text mining analysis of the climate change literature in industrial ecology" published in the Journal of Industrial Ecology.

The data required to run the LiTCoF tool needs to be downloaded ahead of time from the Web of Science in csv format. The code can then convert the data to pkl format unless a pkl file is already given. You can view and download the data used for our review of the climate change research in industrial ecology here.

The code was developed in Python 3. Please contact us if you have any questions about the code.

version 1.00: GitHub | Zenodo | Download Python (.py) | Download iPython (.ipynb) | Tutorial | Publication

version 1.00 beta: GitHub | Download Python (.py) | Download iPython (.ipynb) | Tutorial | Publication

LiTCoF (Literature Topic Co-occurrence and Frequency) Tool Code Picture