textmining-infopros / chapter5

This repository contains a reproducible research compendium for the case study used in Chapter 5 of the book.

Case Study: Network Text Analysis of Documents using Two Different R Packages

This repository contains a reproducible research compendium for the case study used in the book -- Manika Lamba and Margam Madhusudhan (2021) Text Mining for Information Professionals: An Uncharted Territory, SpringerNature.

🔭 Springer Website

🔭 Authors' Book Website

📫 For corrections/suggestions reach me at [e-mail] or create an issue here

How to Cite

Please cite this compendium as: Lamba, Manika, & Madhusudhan, Margam. (2021). Network Text Analysis of Documents using Two Different R Packages (Version 1.2). https://doi.org/10.5281/zenodo.5203302

Contents

The compendium contains the data, code, and notebook associated with the case studies. This case study is further divided into 5A, and 5B. Both 5A and 5B case studies used R programming language to perform text network analysis but with different packages. It is organized as follows:

• The 5a_dataset.txt file contains the data for 5A case study.
  • The 5a_results_coword_network_analysis.csv file contains the supplementary data associated with 5A case study.
  • The 5a_results_main_information.pdf file contains the supplementary data associated with 5A case study.
• The 5b_dataset.csv file contains the data for 5B case study.
  • The 5b_results_centrality.csv file contains the supplementary data associated with 5B case study.
  • The 5b_results_communities.csv file contains the supplementary data associated with 5B case study.
• The text_network_analysis.R file contatins the R code for 5B case study.
• The Case_Study_5B.ipynb file contatins the Jupyter notebook for 5B case study.

How to Download or Install

There are several ways to use the compendium’s contents and reproduce the analysis:

• Download the compendium as a zip archive from this GitHub repository.

  • After unpacking the downloaded zip archive, you can explore the files on your computer.

• Reproduce the analysis in the cloud without having to install any software. The same Docker container replicating the computational environment used by the authors can be run using BinderHub on mybinder.org:

  • Click RStudio: to launch an interactive RStudio session in your web browser for hands-on practice for 5B case study. In the virtual environment, open the text_network_analysis.R file to run the code.

  • Click Jupyter+R: to launch an interactive Jupyter Notebook session in your web browser using R kernel. When you execute code within the notebook, the results appear beneath the code.

  • Limitations of Binder

    1. The server has limited memory so you cannot load large datasets or run big computations.
    2. Binder is meant for interactive and ephemeral interactive coding so an instance will die after 10 minutes of inactivity.
    3. An instance cannot be kept alive for more than 12 hours.

Licenses

Figures, Code, Data, Hex-sticker : MIT License