Metadata-Version: 2.1
Name: autosentiment
Version: 1.0.1
Summary: An automatic sentiment analysis pakage
Home-page: https://github.com/CodeFighter03/autosentiment
Author: Sazin Reshed Samin
Author-email: sazinsamin50@gmail.com
License: UNKNOWN
Description: # Automate sentiment analysis tool
        
        ### Author : Sazin Reshed Samin
        
        * Email : <sazinsamin50@gmail.com>
        
        
        ## autosentiment is an open source library that generates sentiment type(positive,negetive,neutral) pie char,percentage,number and ternary value for pandas dataframe text portion.
        
        
        ##- Usage
        For analysis the seintiment type in positive,negetive or neutral
        
        
        ## - Setup in normal environment and command window:
        ```
        pip install autosentiment
        ```
        
        
        ## - Setup in jupyter notebook:
        ```
        !pip install autosentiment
        ```
        
        
        ## - Import library : 
        ```
        import autosentiment as at
        ```
        
        
        ## - The library is pandas dataframe dependent.
        ```
        Have to get dataframe('text columns') and give to command.
        Like df['text]
        ```
        
        
        
        
        ## Features
        ### - sentiment type pie chart :
        ```
        at.pie()
        ```
        
        ### - sentiment type amount : 
        Get the sentiment type(postive,negetive,neutral numbers)
        ```
        at.number()
        ```
        
        
        ### - sentiment percentage :
        Get the percentage of sentiment type
        ```
        at.percentage()
        ```
        
        
        ### - An example usages
        
        ```
        
        >>import autosentiment as at
        
        >>import pandas as pd
        
        >>df=pd.read_csv("/home/samin/anaconda3/dataset_2.csv")
        
        >>percent=at.percentage(df['text'])
        
        >>print(percent)
        >>Positve : 33.31 %, Negetive 20.96 %, Neutral : 45.72 %
        
        >>number=at.number(df['text'])
        
        >>print(number)
        >>{'positive  ': 1087, 'negetive': 684, 'neutral': 1492}
        
        >>ana=at.analysis_ternary(df['text'])
        
        >>print(ana)
        >>[-1, 1, 0.0, 0.0, 0.0, 0.0,.......,1]
        
        >>at.pie(df['text'])
        
        
        ```
        ![pie chart](/home/samin/Videos/image_12.png)
        
        
        
        
        * For any bug, please notify in my email : <sazinsamin50@gmail.com>
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
