Metadata-Version: 1.1
Name: sentianalyse
Version: 0.0.2
Summary: A small tool for sentiment analysis of texts.
Home-page: https://github.com/garain/sentianalyse
Author: Avishek Garain
Author-email: avishekgarain@gmail.com
License: MIT
Description: # sentianalyse
        -------------------
        
        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
        --------------
        
        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
        ---------
        
        .. 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.
        
        
Keywords: sentiment polarity emotion texts
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Internet
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
