https://github.com/uudigitalhumanitieslab/reader-responses-to-translated-literature

Scripts for the DIOPTRA-L project (Digital Opinions on Translated Literature)
https://github.com/uudigitalhumanitieslab/reader-responses-to-translated-literature

distant-reading translation-studies

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Scripts for the DIOPTRA-L project (Digital Opinions on Translated Literature)

• Host: GitHub
• URL: https://github.com/uudigitalhumanitieslab/reader-responses-to-translated-literature
• Owner: UUDigitalHumanitieslab
• License: mit
• Created: 2020-03-05T07:05:55.000Z (almost 5 years ago)
• Default Branch: develop
• Last Pushed: 2024-03-25T09:50:33.000Z (10 months ago)
• Last Synced: 2024-03-25T10:35:29.054Z (10 months ago)
• Topics: distant-reading, translation-studies
• Language: Python
• Homepage:
• Size: 5.05 MB
• Stars: 0
• Watchers: 4
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# Reader responses to translated literature

This repository contains code for the [DIOPTRA-L](https://cdh.uu.nl/portfolio/digital-opinions-on-translated-literature-dioptra-l-2/) project by Haidee Kotze, Gys-Walt van Egdom, Corina Koolen and Utrecht University's Research Software Lab, and can be used to reproduce the publication

Kotze, Haidee & Janssen, Berit & Koolen, Corina & Plas, Luka & Egdom, Gys-Walt. (2021). _Norms, affect and evaluation in the reception of literary translations in multilingual online reading communities: Deriving cognitive-evaluative templates from big data._ Translation, Cognition & Behavior. 4. 10.1075/tcb.00060.kot.

## Prerequisites

### Python

Most of the scripts require Python 3.6. To install dependencies, run

`pip install -r requirements.txt`

### R

The statistical analysis and visualization was performed in R, using the following libraries:

- coin

- dplyr

- ggplot2

- Hmisc

- irr

- lme4

- reshape2

- rstatix

## Steps to reproduce

1. scrapers: Python scripts used to scrape reviews from Goodreads. Documentation on usage in that folder's README.

2. preprocessing: Python scripts used to clean the data, and more specifically, tokenization.

3. embeddings: Jupyter notebooks for training and evaluating word embeddings using word2vec. As the dataset is relatively small, the resulting embeddings were not informative for further research.

4. analysis: Python scripts to collect and count translation lemmas, based on human annotations.

5. collocations: Python scripts for finding collocations surrounding translation lemmas

6. sentiment: Python scripts to count positive / negative and hedge terms in collocations.

7. model: R scripts used to generate statistics and visualizations of the data.