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https://github.com/dermatologist/nlp-qrmine
Qualitative Research support tools in Python
https://github.com/dermatologist/nlp-qrmine
hacktoberfest interview-data machine-learning nlp nlp-machine-learning python3 qualitative-data-analysis qualitative-research research-tool
Last synced: 14 days ago
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Qualitative Research support tools in Python
- Host: GitHub
- URL: https://github.com/dermatologist/nlp-qrmine
- Owner: dermatologist
- License: gpl-3.0
- Created: 2018-06-29T02:26:30.000Z (over 6 years ago)
- Default Branch: develop
- Last Pushed: 2023-12-11T16:49:55.000Z (11 months ago)
- Last Synced: 2024-04-25T22:01:37.613Z (7 months ago)
- Topics: hacktoberfest, interview-data, machine-learning, nlp, nlp-machine-learning, python3, qualitative-data-analysis, qualitative-research, research-tool
- Language: Jupyter Notebook
- Homepage: http://nuchange.ca/2017/09/grounded-theory-qualitative-research-python.html
- Size: 3.2 MB
- Stars: 45
- Watchers: 7
- Forks: 14
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
Awesome Lists containing this project
README
# :flashlight: QRMine
*/ˈkärmīn/*
[](https://www.python.org/)[](https://pypi.python.org/pypi/qrmine/)
[](https://dermatologist.github.io/nlp-qrmine/)
QRMine is a suite of qualitative research (QR) data mining tools in Python using Natural Language Processing (NLP) and Machine Learning (ML). QRMine is work in progress. [Read More..](https://nuchange.ca/2017/09/grounded-theory-qualitative-research-python.html)
## What it does
### NLP
* Lists common categories for open coding.
* Create a coding dictionary with categories, properties and dimensions.
* Topic modelling.
* Arrange docs according to topics.
* Compare two documents/interviews.
* Select documents/interviews by sentiment, category or title for further analysis.
* Sentiment analysis
### ML
* Accuracy of a neural network model trained using the data
* Confusion matrix from an support vector machine classifier
* K nearest neighbours of a given record
* K-Means clustering
* Principal Component Analysis (PCA)
* Association rules
## How to install
```text
pip install qrmine
python -m spacy download en_core_web_sm
```
### Mac users
* Mac users, please install *libomp* for XGBoost
```
brew install libomp
```
## How to Use
* input files are transcripts as txt files and a single csv file with numeric data. The output txt file can be specified.
* The coding dictionary, topics and topic assignments can be created from the entire corpus (all documents) using the respective command line options.
* Categories (concepts), summary and sentiment can be viewed for entire corpus or specific titles (documents) specified using the --titles switch. Sentence level sentiment output is possible with the --sentence flag.
* You can filter documents based on sentiment, titles or categories and do further analysis, using --filters or -f
* Many of the ML functions like neural network takes a second argument (-n) . In nnet -n signifies the number of epochs, number of clusters in kmeans, number of factors in pca, and number of neighbours in KNN. KNN also takes the --rec or -r argument to specify the record.
* Variables from csv can be selected using --titles (defaults to all). The first variable will be ignored (index) and the last will be the DV (dependant variable).
### Command-line options
```text
qrmine --help
```
| Command | Alternate | Description |
| --- | --- | --- |
| --inp | -i | Input file in the text format with Topic |
| --out | -o | Output file name |
| --csv | | csv file name |
| --num | -n | N (clusters/epochs etc depending on context) |
| --rec | -r | Record (based on context) |
| --titles | -t | Document(s) title(s) to analyze/compare |
| --codedict | | Generate coding dictionary |
| --topics | | Generate topic model |
| --assign | | Assign documents to topics |
| --cat | | List categories of entire corpus or individual docs |
| --summary | | Generate summary for entire corpus or individual docs |
| --sentiment | | Generate sentiment score for entire corpus or individual docs |
| --nlp | | Generate all NLP reports |
| --sentence | | Generate sentence level scores when applicable |
| --nnet | | Display accuracy of a neural network model -n epochs(3)|
| --svm | | Display confusion matrix from an svm classifier |
| --knn | | Display nearest neighbours -n neighbours (3)|
| --kmeans | | Display KMeans clusters -n clusters (3)|
| --cart | | Display Association Rules |
| --pca | | Display PCA -n factors (3)|
## Use it in your code
```python
from qrmine import Content
from qrmine import Network
from qrmine import Qrmine
from qrmine import ReadData
from qrmine import Sentiment
from qrmine import MLQRMine
```
* *More instructions and a jupyter notebook available [here.](https://nuchange.ca/2017/09/grounded-theory-qualitative-research-python.html)*
## Input file format
### NLP
Individual documents or interview transcripts in a single text file separated by Topic. Example below
```
Transcript of the first interview with John.
Any number of lines
First_Interview_John
Text of the second interview with Jane.
More text.
Second_Interview_Jane
....
```
Multiple files are suported, each having only one break tag at the bottom with the topic.
(The tag may be renamed in the future)
### ML
A single csv file with the following generic structure.
* Column 1 with identifier. If it is related to a text document as above, include the title.
* Last column has the dependent variable (DV). (NLP algorithms like the topic asignments may provide the DV)
* All independent variables (numerical) in between.
```
index, obesity, bmi, exercise, income, bp, fbs, has_diabetes
1, 0, 29, 1, 12, 120, 89, 1
2, 1, 32, 0, 9, 140, 92, 0
......
```
## Author
* [Bell Eapen](https://nuchange.ca) (McMaster U) | [Contact](https://nuchange.ca/contact) | [](https://twitter.com/beapen)
* This software is developed and tested using [Compute Canada](http://www.computecanada.ca) resources.
* See also: [:fire: The FHIRForm framework for managing healthcare eForms](https://github.com/E-Health/fhirform)
* See also: [:eyes: Drishti | An mHealth sense-plan-act framework!](https://github.com/E-Health/drishti)
## Citation
Please cite QRMine in your publications if it helped your research. Here
is an example BibTeX entry [(Read paper on arXiv)](https://arxiv.org/abs/2003.13519):
```
@article{eapenbr2019qrmine,
title={QRMine: A python package for triangulation in Grounded Theory},
author={Eapen, Bell Raj and Archer, Norm and Sartpi, Kamran},
journal={arXiv preprint arXiv:2003.13519 },
year={2020}
}
```
QRMine is inspired by [this work](https://github.com/lknelson/computational-grounded-theory) and the associated [paper](https://journals.sagepub.com/doi/abs/10.1177/0049124117729703).
## Give us a star ⭐️
If you find this project useful, give us a star. It helps others discover the project.
## Demo
[](https://github.com/dermatologist/nlp-qrmine/blob/develop/notes/qrmine.gif)