MNPolarity - Mongolian Polarity Detection in Weakly Supervised manner

#nolabel #tweet #classification #snorkel #weaklysupervised #dataprogramming #software2.0

Tweet polarization can be useful in many analysis as an extra factor. However, because of the lack of labeled data in Mongolian (low resource language), it is hard to make progress. So in this repo, we will try to improve our classification model using data programming.

This project is highly dependant on snorkel.ai

Contents:

• Installation
• Usage
• Train Simplest Model
• Structure
• How can you improve
• TODO
• Reference

Installation

git clone https://github.com/bayartsogt-ya/mnpolarity.git && cd mnpolarity
python3 -m venv env && source env/bin/activate
pip install -r requirements.txt

Download latest pre-trained models

wget https://github.com/bayartsogt-ya/mnpolarity/releases/latest/download/0.0.zip -P output/
unzip output/0.0.zip -d output && rm output/0.0.zip

Usage

>>> from mnpolarity.models import SimplestModel
>>> model = SimplestModel()
>>> model.load()
>>> prediction = model.predict("эд нарыг үзэн ядаж байна")  # https://twitter.com/tsbat_IT/status/937989630472761344
>>> prediction["prettier"]
`эд нарыг үзэн ядаж байна` => NEGATIVE (0.96)
>>> prediction
{
  'pred': 1, 
  'label': 'NEGATIVE', 
  'prob': 0.9598129979059339, 
  'prettier': '`эд нарыг үзэн ядаж байна` => NEGATIVE (0.96)'
}

Train Simplest Model

python train_simplest.py

Structure

.
├── ...
├── configs
├── data
│   ├── lf_helpers
│   │   └── negative
│   │       ├── emojis.txt
│   │       ├── phrases.txt
│   │       └── words.txt
│   └── train
│       ├── twint
│       │   ├── bad_word1.csv
│       │   ├── bad_word2.csv
│       │   └── ...
│       └── twitter_dump
│           ├── dump1.csv
│           ├── dump2.csv
│           └── ...
├── mnpolarity
│   ├── labeling_functions
│   │   ├── custom_lfs.py
│   │   └── ...
│   ├── models.py
│   └── ...
└── ...

How can you improve

• Improve Negative List
• Add more custom labeling functions
• Try&Feedback. If you have any request or idea to improve, please email to me

TODO

• Negative list completion
• Positive list creation
• Negative list completion
• Labeling Function addition
  • The MOST IMPORTANT part of this code is labeling functions build
• Experiment with hand labeled data
• Create (well validated) 1000-row test set

Reference

• Data Programming read more
• Software 2.0 read more

Citation

@misc{mnpolarity,
  author = {Bayartsogt Yadamsuren},
  title = {Mongolian Polarity Detection in Weakly Supervised manner},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/bayartsogt-ya/mnpolarity/}}
}