sentianalyse

sentianalyse

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A simple python library that generates sentiment type(positive,negetive,neutral) pie chart, percentage,number and ternary value for pandas dataframe text portion.

The code is Python 2 and 3 compatible.

Installation

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Fast install:

:: pip install sentianalyse

For a manual install get this package:

.. code:: bash

    $wget https://github.com/garain/sentianalyse/archive/master.zip
    $unzip master.zip
    $rm master.zip
    $cd sentianalyse-master

Install the package:

::

    python setup.py install    

The library is pandas dataframe dependent.

---

:: Have to get dataframe('text columns') and give to command. Like df['text']

Example

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.. code:: python

    import sentianalyse as sa
	# Features
	
    # - sentiment type pie chart :
    sa.pie()

    
    # sentiment type amount : 
    # - Get the sentiment type(postive,negetive,neutral numbers)
    sa.number()
           
    
    # sentiment percentage :
    # - Get the percentage of sentiment type
    sa.percentage() 
            
    
    # sa.ternary_analysis
    # - Get the type of all text, here -1:negetive, 0:neutral, 1:positive
    sa.ternary_analysis()
           
       
    import pandas as pd
    
    df=pd.read_csv("/home/samin/anaconda3/dataset_2.csv")
    
    percent=at.percentage(df['text'])
    
    print(percent)
    
    
    number = sa.number(df['text'])
    
    print(number)
    
    
    analysis = sa.analysis_ternary(df['text'])
    
    print(analysis)
    
    
    #sa.pie(df['text'])
	
    # Pass list of texts as input
	
	df=pd.DataFrame(["I love you very much."],columns=['text'])

Here is the output:

::

Positve : 33.31 %, Negetive 20.96 %, Neutral : 45.72 %
{'positive  ': 1087, 'negetive': 684, 'neutral': 1492}
[-1, 1, 0.0, 0.0, 0.0, 0.0,.......,1]

Please cite these publications if this library comes to any use:

• Ray, Biswarup, Avishek Garain, and Ram Sarkar. "An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews." Applied Soft Computing 98 (2021): 106935.
• Garain, Avishek, and Sainik Kumar Mahata. "Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet Level Using Deep Learning." (2019).
• Garain, Avishek, and Arpan Basu. "The titans at SemEval-2019 task 5: Detection of hate speech against immigrants and women in twitter." Proceedings of the 13th International Workshop on Semantic Evaluation. 2019.
• Garain, Avishek. "Humor Analysis based on Human Annotation (HAHA)-2019: Humor Analysis at Tweet Level using Deep Learning." (2019).
• Garain, Avishek, and Arpan Basu. "The titans at SemEval-2019 task 6: Offensive language identification, categorization and target identification." Proceedings of the 13th International Workshop on Semantic Evaluation. 2019.