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Analysis of skills and the skills taxonomy

This analysis folder contains analysis and experimentation notebooks for 5 themes:

  1. tk_analysis/
  2. sentence_classifier/
  3. skills_extraction/
  4. skills_taxonomy/
  5. skills_taxonomy_application/

Outputs (figures and data) from analysis are saved to a corresponding folder in the outputs/ folder.

tk_analysis/

This folder contains two notebooks:

  1. TextKernel Data.ipynb - Provides a summary of TextKernel dataset.
  2. TextKernel Data Sample for Skills.ipynb - Comparison of our sample of TextKernel data to all data.

Outputs are in outputs/tk_analysis.

sentence_classifier/

This folder contains three markdown files:

  1. Sentence Splitter Experiments.md - Notes on experiments for the initial skills classifier
  2. skills_classifier_2.0_experiments.md - Notes on experiments for the skills classifier after labelling more data
  3. skills_codebook.md - A codebook for labellers to follow when labelling sentences as either skill sentences or not.

Within notebooks, there's a number of .ipynb and .py files related to experimentation:

  1. Skills Classifier 1.0 - Analyse sentence predictions.py - initial experiments from skills classifier 1.0
  2. Skills Classifier 1.0 - Doccano Baseline Classifier.py - initial experiments from skills classifier 1.0
  3. Skills Classifier 1.0 - TextKernel Automated Training Data.py - initial experiments from skills classifier 1.0
  4. Skills Classifier 1.0 - Sentence Classifier.py - initial experiments from skills classifier 1.0
  5. Skills Classifier 2.0 - improve_classifier.ipynb - experiments using more labelled data from skills classifier 2.0

skills_extraction/

In this folder we have two scripts for various bits of analysis and figure plotting after extracting skills:

  1. Multi-skill sentences.pynb - Analysis of multi-skill sentence - how long are sentences typically when they have multiple skills in.
  2. Experiment - Data reduction sample size.ipynb - Analysis of finding a good sample size of embeddings to fit the reducer class to.
  3. Effect of sample size.ipynb - Investigate the effect of sample size of skill sentences and how many words are in the vocab.
  4. Experiment - Clustering parameters.ipynb - Analysis of which are the optimal clustering parameters to extract skills from.
  5. Effect of merging clusters distance threshold.ipynb - Investigation of the distance threshold for which two skill clusters should be merged into one.
  6. Skills Extraction Analysis and Figures.ipynb - Various analysis and figure generation of the skills extracted. Outputs are in outputs/skills_extraction/figures/..
  7. Extracted skills - 2021.11.05.py

In this folder we also have experimentation notebooks showing 4 approaches for skills extraction approaches, including:

  1. Network approach
  2. Transformers sentence embeddings approach - no masking
  3. Word2vec approach
  4. Transformers sentence embeddings approach - masking

The last approach was what we used in the final pipeline (refactored in skills_taxonomy_v2/pipeline/skills_extraction/).

skills_taxonomy/

In this folder we perform some analysis on the skills taxonomy created when running skills_taxonomy_v2/pipeline/skills_taxonomy/build_taxonomy.py.

  1. Evaluate hierarchy.ipynb - Evaluate hierarchy based on popular skill groups for job titles. Output csvs stored in outputs/skills_taxonomy/evaluation/.
  2. Tranversal Skills.ipynb - Identify the most and least transversal skills and skill groups. Outputs in outputs/skills_taxonomy/transversal/.
  3. Renaming sample of skill groups.ipynb - Manually creating names for some of the skill groups, outputs skills_taxonomy_v2/utils/2021.09.06_level_a_rename_dict.json which is used in other notebooks.
  4. Skills Taxonomy Analysis and Figures.ipynb - this notebook provides some visualisation and analysis of it. Outputs are stored in outputs/skills_taxonomy/figures/2021.09.06/.
  5. Plot interactive hierarchy.ipynb - this notebook plot interactive plots of the hierarchy for interrogation.

skills_taxonomy_application/

This folder contains two notebooks to analyse the skills taxonomy in application to the job location and whether the job was advertise pre or post COVID.

  1. Application - Geography.ipynb See how different skill groups in the taxonomy relate with location of the job advert. Outputs in outputs/skills_taxonomy_application/region_application/.
  2. Application - COVID.ipynb See how different skill groups in the taxonomy relate with whether the job advert was out pre or post COVID. Outputs in outputs/skills_taxonomy_application/covid_application.

This folder also contains the script locations_to_nuts2.py to convert longitude and latitude coordinates from the job adverts to NUTS2 regional classifications - this was neccessary for use in Application - Geography.ipynb .