Case Study: Sentiment Analysis of Documents using Two Different Tools

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 lambamanika07@gmail.com or create an issue here

How to Cite

Please cite this compendium as: Lamba, Manika, & Madhusudhan, Margam. (2021). Sentiment Analysis of Documents using Two Different Tools (Version 1.0). http://doi.org/10.5281/zenodo.5090224

Contents

The compendium contains the data, code, and notebook associated with the case studies. It is divided into 7A, and 7B. 7A case study used RapidMiner, and 7B case study used R programming language to perform sentiment analysis. It is organized as follows:

• The 7a_processed_dataset.rar contains the processed data for 7A case study.
• The 7b_dataset.csv file contains the data for 7B case study.
  • The negative_book_reviews.csv file contains the supplementary data associated with 7B case study.
  • The neutral_book_reviews.csv file contains the supplementary data associated with 7B case study.
  • The positive_book_reviews.csv file contains the supplementary data associated with 7B case study.
• The sentiment_analysis.R file contatins the R code for 7B case study.
• The Case_Study_7B.ipynb file contatins the Jupyter notebook for 7B 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 6B case study. In the virtual environment, open the sentiment_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

Code, Data: MIT License