Sentiment Analysis

About

Chapter 7: Sentiment Analysis covers the case study on Sentiment Analysis of Documents using Two Different Tools. It is divided into two parts: 7A, and 7B. 7A case study uses RapidMiner, whereas 7B case study uses R programming language to perform sentiment analysis.

How to Cite

Please cite this compendium as: Lamba, Manika, & Madhusudhan, Margam. (2021). Sentiment Analysis of Documents using Two Different Tools (Version 1.1). https://doi.org/10.5281/zenodo.5203347

Contents

The compendium contains the data, code, and notebook associated with the case studies. 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.

In addition to the provided sample data, you can use dataset from Appendix A, Appendix B, Appendix C, Curated Datasets, or your own dataset to perform sentiment analysis.

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 the 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.

Visualize the Results

A storyboard is built to summarize the visualizations for 13 case studies performed in the book. To know more about the case studies, and the methodology used to get the results, read the book.

Results for 7A Case Study

Results for 7B Case Study

Licenses

Text and Figures: ©2021 Lamba and Madhusudhan - all rights reserved, unless stated otherwise.

Code, Data, Hex-sticker: MIT License