An automatic sentiment analysis pakage
Project 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()
at.ternary_analysis
Get the type of all text, here -1:negetive, 0:neutral, 1:positive
at.ternary_analysis()
- 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'])
- For any bug, please notify in my email : sazinsamin50@gmail.com
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
autosentiment-1.2.6.tar.gz
(2.9 kB
view hashes)
Built Distribution
Close
Hashes for autosentiment-1.2.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cd6961f1d0f0ba64848470c6d80eb7eef443aef2e51beae2488740f53436dea1 |
|
MD5 | 508ce7f6e9cfba3db99898799955b646 |
|
BLAKE2b-256 | 04b3d51530c67515053afa8218067186ed997575305de8af93c93cf99ec5f77e |