sentivi 1.1.0

A Simple Tool For Sentiment Analysis

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.

Documentation: https://sentivi.readthedocs.io/en/latest/index.html

Classifiers

• Decision Tree
• Gaussian Naive Bayes
• Gaussian Process
• Nearest Centroid
• Support Vector Machine
• Stochastic Gradient Descent
• Character Convolutional Neural Network
• Multi-Layer Perceptron
• Long Short Term Memory
• Text Convolutional Neural Network
• Transformer
• Ensemble
• Lexicon-based

Text Encoders

• One-hot
• Bag of Words
• Term Frequency - Inverse Document Frequency
• Word2Vec
• Transformer Tokenizer (for Transformer classifier only)
• WordPiece
• SentencePiece

Install

• Install legacy 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='./data/dev.vi', test='./data/dev_test.vi')
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)}')

Take a look at more examples in example/.

Pipeline Serving

Sentivi use FastAPI to serving pipeline. Simply run a web service as follows:

# serving.py
from sentivi import Pipeline, RESTServiceGateway

pipeline = Pipeline.load('./weights/pipeline.sentivi')
server = RESTServiceGateway(pipeline).get_server()

# pip install uvicorn python-multipart
uvicorn serving:server --host 127.0.0.1 --port 8000

Access Swagger at http://127.0.0.1:8000/docs or Redoc http://127.0.0.1:8000/redoc. For example, you can use curl to send post requests:

curl --location --request POST 'http://127.0.0.1:8000/get_sentiment/' \
     --form 'text=Son đẹpppp, mùi hương vali thơm nhưng hơi nồng'

# response
{ "polarity": 2, "label": "#POS" }

Deploy using Docker

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY . /app

ENV PYTHONPATH=/app
ENV APP_MODULE=serving:server
ENV WORKERS_PER_CORE=0.75
ENV MAX_WORKERS=6
ENV HOST=0.0.0.0
ENV PORT=80

RUN pip install -r requirements.txt

docker build -t sentivi .
docker run -d -p 8000:80 sentivi

Future Releases

• Lexicon-based
• CharCNN
• Ensemble learning methods
• Model serving (Back-end and Front-end)