semeval2023-afrisenti

A low-resource sentiment analysis project for African Languages

Running the code

1. Clone the repo and cd to project directory
2. Install python -- version 3.8.12
3. Run the command python -m venv .venv to setup the virtual environment
4. Activate the virtual environment using source .venv/bin/activate
5. Install the requirements pip install -r requirements.txt
6. To run lexicon-based sentiment analysis for Igbo language, go to "Lexicon-based SA for Igbo" section below

Training the model

The following commandline options are available for training the model

Usage: python -m src.models.train_model [OPTIONS]

Options:
  --lang [am|dz|ha|ig|ma|pcm|pt|sw|yo]
  --model [LinearSVM|NaiveBayes|naija-roberta-large|xlm-roberta-small]
  --finetune_lm                   Finetune the language model as well
  --finetune_classifier           Finetune classification layer
  --help                          Show this message and exit.

The --model option indicates which model to train.

The --finetune_classifier would fine tune the pretrained model on the training data. It is only applicable is the model is either naija-roberta-large or xlm-roberta-small. When LinearSVC or NaiveBayes is selected this option is ignored.

The --finetune_lm option will finetune the masked language model objective with the traning data for that particular model. It is always used with --finetune_classifier.

Example Usage: python -m src.models.train_model --lang="pcm" --model="naija-roberta-large" --finetune_classifier --finetune_lm

Evaluating the model

The following commandline options are available for evaluating the model

Usage: python -m src.models.predict_model [OPTIONS]

Options:
  --lang [am|dz|ha|ig|ma|pcm|pt|sw|yo]
  --model [LinearSVM|NaiveBayes|naija-roberta-large|xlm-roberta-small]
  --finetune_classifier           Use finetuned classification layer
  --help                          Show this message and exit.

The --model option indicates which model to train.

The --finetune_classifier will use the model finetuned on the training data. It is only applicable is the model is either naija-roberta-large or xlm-roberta-small. When LinearSVC or NaiveBayes is selected this option is ignored.

Example Usage: python -m src.models.predict_model --lang="pcm" --model="naija-roberta-large" --finetune_classifier

Lexicon-based SA for Igbo

To do lexicon-based sentiment analysis for Igbo language, cd to semeval2023-afrisenti root folder in cmd, create a virtual environment and install necessary libraries like transformers, pandas, sklearn, and then type "py ig_lexicon_based_submit.py" and hit enter. The experiments will be run.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience