Metadata-Version: 2.1
Name: autosentiment
Version: 0.4.4
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 <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 : 
        ```
        at.number()
        ```
        
        
        - sentiment percentage :
        ```
        at.percentage()
        ```
        
        
        - An example usages
        ```
        >>import autosentiment as at
        >>import pandas as pd
        
        >>df=pd.read_csv("dataset_2.csv")
        
        
        >>number=at.number(df['text'])
        >>print(number)
        
        >>{'postive': 1087, 'negetive': 684, 'neutral': 1492}
        
        >>percen=at.percentage(df['text'])
        >>print(percen)
        >>postive': 33.31, 'negetive': 20.96, 'neutral': 45.72}
        
        
        >>analysis=at.analysis_ternary(df['text'])
        >>analysis
        
        >> [1,
         0.0,
         1,
         1,
         1,
         -1,
         1,
         -1,
         0.0,
         ...]
        ```
        
        
        
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
