Metadata-Version: 2.1
Name: sentivi
Version: 1.0.0
Summary: A simple tool for Vietnamese Sentiment Analysis
Home-page: https://github.com/vndee/sentivi
Author: Duy V. Huynh
Author-email: hvd.huynhduy@gmail.com
License: MIT
Description: ## A Simple Tool For Sentiment Analysis
        
        **Sentivi** - a simple tool for sentiment analysis which is a wrapper of [scikit-learn](https://scikit-learn.org) and
        [PyTorch](https://pytorch.org/) models. It is made for easy and faster pipeline to train and evaluate several
        classification algorithms.
        
        Documentation: https://sentivi.readthedocs.io/en/latest/index.html
        
        ### Install
        - Install legacy version from PyPI:
            ```bash
            pip install sentivi
            ```
        
        - Install latest version from source:
            ```bash
            git clone https://github.com/vndee/sentivi
            cd sentivi
            pip install .
            ```
        
        ### Example
        
        ```python
        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='./data/dev.vi', test='./data/dev_test.vi',
                                 save_path='./weights/svm.sentivi')
        print(train_results)
        
        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)}')
        ```
        Take a look at more examples in [example/](https://github.com/vndee/sentivi/tree/master/example).
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
