CohPy

CohPy is an easy-to-use pure python pipeline, to measure textual features and with them assess textual comprehensibility. Currently CohPy is usable for two languages, english and german. It supplies a crawl feature for the gutenberg project, and two categories of additional texts. The great advantage is its open source code and thereby enabling additional implementations/modifications of scores and languages.

Target group

The aim of this project, is to supply everybody, working with texts and text comprehension/cohesion, to have a tool at their disposal.

Get Started:

• Some of the scores require additional dependencies. If those dependencies are not available, the remaining workflow is still operational!
  • V2W model of sentiment scores: we received the SentiArt model (Jacobs, A. M., Kinder, A. (2019). Computing the Affective-Aesthetic Potential of Literary Texts)
  • Affective Norm scores: Available at the University of Stuttgarts Institute for Natural Language Processing (https://www.ims.uni-stuttgart.de/en/research/resources/experiment-data/twitter-norms/)
  • Word Frequencies: Available at the University of Leipzig Leipzig Corpora Collection (https://wortschatz.uni-leipzig.de)
  • List of Connective words, by categories - A preliminary self compiled list is available at data/Score_files
• The installation of the TreeTagger is also necessary (https://cis.uni-muenchen.de/~schmid/tools/TreeTagger/)
  • Alternatively, a different POS Tagger and Lemmatizer can be used, by changing the implementation slightly. The relevant function can be found at Helper/Helper_function.py, called POS_tagger.
• Clone github repository
• install all necessary dependencies (conda env create -f environment.yml)
• running the application:
  • It is recommended to keep the folder structure suggested below.
    • Otherwise quite some paths have to be adapted
  • make sure that your current working directory is at the same level as the main.py script
  • As mentioned earlier, this tool provides a download possibility for the gutenberg project, although this is not possible from a german Network address! (at least in March 2021)
    • Note that this process requires 10h +
  • with bool variables, it can be checked, which folders should be processed.
  • If some of the dependencies are not given, set them to None (for each language where it is applicable)
• for specifics, each function is labeled as what its input is and what it does

Folder Structure for the data folder:

• save additional dependency files in the Score_files folder
  • Affective_Norm_LANGUAGE_IN_2_LETTER_CODE.csv
  • Word_Frequency_LANGUAGE_IN_2_LETTER_CODE.csv
  • Connectives_LANGUAGE_IN_2_LETTER_CODE.csv
  • Sentiment_v2w.vec - using SentiArt, contains english and german words
• If no changes are made, the Gutenberg data is saved to a folder Gutenberg, containing the metadata file and a subfolder txt_files, where the Gutenberg Project Books are saved into, with their ID as identifier.
• Extra_books and New_documents folders contain a subfolder for each language (also with LANGUAGE_IN_2_LETTER_CODE)
• ML Results is generated to save all results of the Classification/Regression Task
• The Result files of the scoring pipeline are saved at the target_paths for the categories (can be set at the main function) default location is data/

Adding new Scores:

• Implement the score
• import into the Pipeline.py script
• place at an appropriate position/end
• add the results to the result_dict

Adding new Languages:

• Add new Tagsets: see Tagset_LANGUAGE_IN_2_LETTER_CODE.py
• Import Tagsets in the Pipeline (at "Load Tagsets from Tagset_LANGUAGE.py" )
• Add the dependency for the treeTagger
• call the main function, with the respective additional language specific dependencies