Metadata-Version: 2.1
Name: persian-sa
Version: 0.2.5
Summary: Python Machine Learning based API to predict sentiment for Persian text.
Home-page: https://github.com/kasrahabib/persian-sentiment-analysis
Author: Mohammad Kasra Habib
Author-email: kasrahabib@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Topic :: Text Processing
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: Persian
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.18.4)
Requires-Dist: scipy (>=1.4.1)
Requires-Dist: scikit-learn

# Persian Sentiment Analysis 
A trained model to predict sentiment class of a given Persian text.

# Installation

```bash
pip3 install persian_sa

````

# Read More:
To find about preprocessing and feature engineering, and how the model predicts visit [arXiv](https://arxiv.org/abs/2101.08087).


# Usage:

## Running the source code
- To run the program, use python3 persian_sa.py
- Next you will be prompted to give a Persian text as input.
- To exit the program write ```exit``` on terminal.

 ```bash

MOHAMMADs:persian_sa mohammadkasra$ python3 persian_sa.py 


This app uses ML to predict setntiment (e.g., Positive or Negative)
of a given Persian text. Toexit  the app write  'exit' in terminal.


Input: زیاد در خاطرات دیگران ورود نکنید، چرا که در خاطرات هر شخص رازهایی وجود دارد که حتی می ترسد آن ها را برای خودش آشکار کند!
... Negative!


Input: زندگی همچون یک آینه است زمانی که در آن لبخند بزنیم شگفت انگیزترین نتایج را به دست خواهیم آورد
... Positive!


Input: exit
...  exit: 0
MOHAMMADs:persian_sa mohammadkasra$ 

```

## Running with after Pip install

```python

>>> from persian_sa.persian_sa import persian_sa
>>> 
>>> persian_sa.predict_sentiment('می تواند به همین دلیل از آن متنفر باشد')
'Negative!'
>>> # Or you can predict the class number; if you set "return_class_label = True"
>>> persian_sa.predict_sentiment('می تواند به همین دلیل از آن متنفر باشد', return_class_label = True)
0
>>> persian_sa.predict_sentiment('اجرای آنها شادی مطلق است')
'Positive!'
>>> persian_sa.predict_sentiment('اجرای آنها شادی مطلق است', return_class_label = True)
1
>>>

```


## CHANGE LOG:

++++++++++++++++++++++CHANGE LOG+++++++++++++++++++++
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..... Version 0.2.3 -->  26.01.2021  -->  1st Release
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