textalyzer 0.1.6

A text analyzer for repeating and contraction characters

# Textalyzer
Textalyzer is a lightweight python module or library to help ease analyzing of text or words in a particular sentence and manipulating it.
Textalyzer analyze repeating words, contracted words or contractions and formatting them properly.

# Class Functions Involved in this module
[-] ContractionReplacers <br>
[-] RepeatingReplacers

# Usage
-Place the file in a folder where you have either read or write and read permisson. Eg. /opinionmining/lib/Textalyzer.py<br/>
-Import the module into your application.<br/>
<hr/>
from opinionmining.lib.Textalyzer import "Function To Use" <br>
sample = "I can't wait for the football match" <br>
object = ContractionReplacers() <br>
data = object.text_replacers(sample)<br>

# Sample
## Repeating words
- Repeating words like helloooooooooooo - textalyzer help format this word by removing the o's to its normal word "hello"<br/>
- Contractions like I'm - Textalyzer help format this word by converting it to it's normal word "I am"
# Dependencies
- wordnet
- Install wordnet by installing nltk
# PyPI
- Textalyzer is available at python package index.
## Installation Guide
- Download textalyzer using pip<br>
- pip install textalyzer<br>
- Import textalyzer into your project by using ```from textalyzer import Textalyzer``` depending on the function you want to use.<br>
   - The use ``` replacer = Textalyzer.ContractionReplacers() and repeat = Textalyzer.RepeatingReplacers() ``` <br>
   - ## Or
- Simple do ```import textalyzer``` <br>
- Then Use ``` data = textalyzer.Textalyzer ``` <br>
- ``` replacer = data.ContractionReplacers() ``` <br>

## Alternatively
- [x] You can download the tarball file from `pypi.python.org/pypi/textalyzer`
- [x] Extract the tarball file
- [x] Navigate into the extracted folder and install using `python setup.py install`

**To Check Whether it's installed**
- type `pip freeze` to display the list of installed packages
# Why This
- Textalyzer is mostly used by python programmers who are more involved with Natural Language Processing , Machine Learning, Text Extraction and others.