Sentivi

A simple tool for sentiment analysis which is a wrapper of scikit-learn and PyTorch Transformers models (for more specific purpose, it is recommend to use native library instead). It is made for easy and faster pipeline to train and evaluate several classification algorithms.

  • Install standard version from PyPI:

pip install sentivi

  • Install latest version from source:

git clone https://github.com/vndee/sentivi
cd sentivi
pip install .

Example:

from sentivi import Pipeline
from sentivi.data import DataLoader, TextEncoder
from sentivi.classifier import SVMClassifier
from sentivi.text_processor import TextProcessor

text_processor = TextProcessor(methods=['word_segmentation', 'remove_punctuation', 'lower'])

pipeline = Pipeline(DataLoader(text_processor=text_processor, n_grams=3),
                    TextEncoder(encode_type='one-hot'),
                    SVMClassifier(num_labels=3))

train_results = pipeline(train='./train.txt', test='./test.txt')
print(train_results)

pipeline.save('./weights/pipeline.sentivi')
_pipeline = Pipeline.load('./weights/pipeline.sentivi')

predict_results = _pipeline.predict(['hàng ok đầu tuýp có một số không vừa ốc siết. chỉ được một số đầu thôi .cần '
                                    'nhất đầu tuýp 14 mà không có. không đạt yêu cầu của mình sử dụng',
                                    'Son đẹpppp, mùi hương vali thơm nhưng hơi nồng, chất son mịn, màu lên chuẩn, '
                                    'đẹppppp'])
print(predict_results)
print(f'Decoded results: {_pipeline.decode_polarity(predict_results)}')

Console output:

One Hot Text Encoder: 100%|██████████| 6/6 [00:00<00:00, 11602.50it/s]
One Hot Text Encoder: 100%|██████████| 2/2 [00:00<00:00, 4966.61it/s]
Input features view be flatten into np.ndarray(6, 35328) for scikit-learn classifier.
Training classifier...
Testing classifier...
Training results:
              precision    recall  f1-score   support

           0       1.00      0.00      0.00         1
           1       0.75      1.00      0.86         3
           2       1.00      1.00      1.00         2

    accuracy                           0.83         6
   macro avg       0.92      0.67      0.62         6
weighted avg       0.88      0.83      0.76         6

Test results:
              precision    recall  f1-score   support

           1       1.00      1.00      1.00         1
           2       1.00      1.00      1.00         1

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2


Saved model to ./weights/pipeline.sentivi
Loaded model from ./weights/pipeline.sentivi
Input features view be flatten into np.ndarray(2, 35328) for scikit-learn classifier.
[2 1]
Decoded results: ['#NEG', '#POS']
One Hot Text Encoder: 100%|██████████| 2/2 [00:00<00:00, 10796.15it/s]

Contents: