Metadata-Version: 2.1
Name: brwording
Version: 0.1.3
Summary: brwording - Processamento de Linguagem Natural em Português
Home-page: https://github.com/TheScientistBr/BRWording
Author: Delermando Branquinho Filho
Author-email: delermando@gmail.com
License: MIT
Description: ## `BRWording` - Text Analytics for Portuguese Wordings
        
        Create an easy Text Analytics in *`One-Line-Code`*
        
        <hr>
        
        ![](https://img.shields.io/badge/pypi-3.3.0-blue) ![](https://img.shields.io/badge/python-3.0|3.0|3.0-lightblue) ![](https://img.shields.io/badge/Licence-MIT-lightgray) ![](https://img.shields.io/badge/status-Beta-darkgreen) ![](https://img.shields.io/badge/pipeline-passed-green) ![](https://img.shields.io/badge/testing-passing-green) ![](https://img.shields.io/badge/TheScientist-APP-brown)
        
        
        **Main Features:**
        
        - Load `Excel`, `CSV` and `TXT` file types
        - Stemming
        - Lemmatization
        - Stopwords
        - TD-IDF
        - Sentimental Analysis
        - Graphical interpretation
        - Word Cloud
        
        The TF-IDF was calculated by:
        
        ![img](https://github.com/TheScientistBr/BRWording/blob/main/images/tf-idf.png?raw=true)
        
        <hr>
        
        ## How to Install
        
        ```shell
        pip install BRWording
        pip install pdfminer-six
        ```
        
        <BR>
        <hr>
        <BR>
        
        ## How to use
        
        `sintax`:
        ```python
        from brwording.brwording import wording
        
        w = brwording.wording()
        
        w.load_file('data/example.txt',type='txt')
        w.build_tf_idf(lemmatizer=True,stopwords=True)
        
        w.tfidf
        
        ```
        
        The fields to `load_file` are:
        3. `file`: the file path 
        3. `type`: file type, can be `txt csv` or `excel`
        3. `header`: if you are reading a csv file, so you must tell if this file has a header or not (`False` or `True`)
        0. `sep`: if you are reading a csv file, you must tell what kind field separator you want
        0. `column`: if you read a `csv` or `excel`file, you must tell what column you want to parse
        
        The method `build_tf_idf` has a default `True`option for both parameters.
        
        **Output**
        
        ![img](https://github.com/TheScientistBr/BRWording/blob/main/images/tfidf.png?raw=true)
        
        If want to see the sentimental Graphical interpretation
        
        `sintax`:
        ```python
        
        w.sentimental_graf()
        
        ```
        You can rotate the graph if you pass `rotate=True` in argument
        
        **output**
        
        ![img](https://github.com/TheScientistBr/BRWording/blob/main/images/graf_sentimental.png?raw=true)
        
        You can print the same information as a table using the follow command:
        
        
        `sintax`:
        ```python
        
        w.sentimental_table()
        
        ```
        
        <br>
        
        if you want to create a wordcloud, just strike the folowing command, but if you want to create a cloud with your own mask, just pass you image address as `picture`
        
        `sintax`:
        ```python
        w.word_cloud(picture='none')
        
        ```
        
        **output**
        
        ![img](https://github.com/TheScientistBr/BRWording/blob/main/images/wc.png?raw=true)
        
        <hr>
        <BR>
        
        **Looking for a word into colection**
        
        if you want to see what files on your colection has a word, run `look3word` 
        
        `sintax`:
        ```python
        w.look3word('bonito')
        
        ```
        
        <BR>
        
        New features are incoming.
        
        <hr>
        <BR>
        
        `enjoi!`
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Description-Content-Type: text/markdown
