sentianalyse 0.0.2

# 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:

$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

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.