https://github.com/vndee/sentivi
A Simple Tool For Sentiment Analysis
https://github.com/vndee/sentivi
deep-learning machine-learning sentiment-analysis text-classification
Last synced: 4 months ago
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A Simple Tool For Sentiment Analysis
- Host: GitHub
- URL: https://github.com/vndee/sentivi
- Owner: vndee
- License: mit
- Created: 2020-09-25T10:12:16.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-12-26T17:14:43.000Z (10 months ago)
- Last Synced: 2025-06-02T11:07:21.560Z (5 months ago)
- Topics: deep-learning, machine-learning, sentiment-analysis, text-classification
- Language: Python
- Homepage: https://sentivi.readthedocs.io/
- Size: 197 KB
- Stars: 16
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## A Simple Tool For Sentiment Analysis
[](https://pepy.tech/projects/sentivi)
**Sentivi** - a simple tool for sentiment analysis which is a wrapper of [scikit-learn](https://scikit-learn.org) and
[PyTorch Transformers](https://huggingface.co/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
- [x] Decision Tree
- [x] Gaussian Naive Bayes
- [x] Gaussian Process
- [x] Nearest Centroid
- [x] Support Vector Machine
- [x] Stochastic Gradient Descent
- [ ] Character Convolutional Neural Network
- [x] Multi-Layer Perceptron
- [x] Long Short Term Memory
- [x] Text Convolutional Neural Network
- [x] Transformer
- [ ] Ensemble
- [ ] Lexicon-based
### Text Encoders
- [x] One-hot
- [x] Bag of Words
- [x] Term Frequency - Inverse Document Frequency
- [x] Word2Vec
- [x] Transformer Tokenizer (for Transformer classifier only)
- [ ] WordPiece
- [ ] SentencePiece
### Install
- Install standard 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')
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/](https://github.com/vndee/sentivi/tree/master/example).
### Pipeline Serving
Sentivi use [FastAPI](https://fastapi.tiangolo.com/) to serving pipeline. Simply run a web service as follows:
```python
# serving.py
from sentivi import Pipeline, RESTServiceGateway
pipeline = Pipeline.load('./weights/pipeline.sentivi')
server = RESTServiceGateway(pipeline).get_server()
```
```bash
# 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](https://curl.haxx.se/) to send post requests:
```bash
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
```dockerfile
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
```
```bash
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)