Reader responses to translated literature

This repository contains code for the DIOPTRA-L 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.