keytext 0.1

Keyword based text mining Pacakage (keytxt)

keyword based text extraction toolkit (keytext)

The basic function of keytext is to fetching important pieces of text, whatever industry you are working on. This smart toolkit able to collect the keyword based texts indisputably.

Installation Procedure

pip install keytext

Dependent Libraries:

This module depends on regex and pandas. Before running install this dependencies.

The functions used here are as follows:

neighbourhood_words

- This function extract the keyword along with left and right neghbouthood words
- import keytxt.neighbourhood_words 
- Parameters are keyword, text, left, right 

left_texts

- This function extract the left part of the keyword in a given sentence
- import keytxt.left_texts
- Parameters are keyword, text, occurence
- If a particular keyword has repeatation then the parameter occurence control the output
- Occurence must be greater than 0

right_texts

- This function extract the rightpart of the keyword in a given sentence.
- import keytxt.right_texts
- Parameters are keyword, text occurence
- If a particular keyword has repeatation then the parameter occurence control the output
- Occurence must be greater than 0

between_fixed_keyword

- This function extract the information between same keywords
- import keytxt.between_fixed_keyword
- Parameters are keyword, text

keyword_position

- This function extract the all matched keyword's start and end positions
- import keytxt.keyword_position
- Parameters are keyword, text

neighbourhood_chr

- This function extract the keyword's along with left and right neghbouthood charecters
- import keytxt.neighbourhood_words
- Parameters are keyword, text, left_chr, right_chr

dataframe_keyword_remover

- This function remove the keyword from the dataframe
- Non alphanumeric charecters need to be write in regex format
- import keytxt.dataframe_keyword_remover
- Parameters are remover_list, dataframe, replaced_by

text_keyword_remover

- This function remove the keyword along with non-alphanumerics from a long text
- import keytxt.text_keyword_remover
- Parameters are remover_list, text, replaced_by

get_freq

- This function works on a base. The base can be 'chr' for charecter and 'word' for words
- import keytxt.get_freq
- Parameters are text, base

Documentation:

# import library
import keytxt

# define text and keyword
text = "Python is (commonly) used for developing website$ and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances."
keyword = "python"

# neighbourhood words of the keyword
keytxt.neighbourhood_words(keyword, text, 1, 3)

['PYTHON IS (COMMONLY) USED', 'LEARN, PYTHON HAS BEEN ADOPTED']

# neighbourhood charecters of the keyword
keytxt.neighbourhood_chr(keyword, text, 3, 4)

['', 'N, PYTHON HAS']

# positions of the keyword
keytxt.keyword_position(keyword, text)

[(0, 6), (157, 163)]

# when keyword is repeating then print the between texts
keytxt.between_fixed_keyword(keyword, text)

[" IS (COMMONLY) USED FOR DEVELOPING WEBSITE$ AND SOFTWARE, TASK AUTOMATION, DATA ANALYSIS, AND DATA VISUALIZATION. SINCE IT'S RELATIVELY EASY TO LEARN, ",
 ' HAS BEEN ADOPTED BY MANY NON-PROGRAMMERS SUCH AS ACCOUNTANTS AND SCIENTISTS, FOR A VARIETY OF EVERYDAY TASKS, LIKE ORGANIZING FINANCES.']

# left texts of 2nd occurence of keyword
keytxt.left_texts(keyword, text, 2)

"Python is (commonly) used for developing website$ and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, "

# right texts of 2nd occurence of keyword
keytxt.right_texts(keyword, text, 1)

" is (commonly) used for developing website$ and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances."

# remove user defined unnecessary phrases from your text data
remover = ['\$', '\)', '\(', 'variety']
keytxt.text_keyword_remover(remover, text, '')

"Python is (commonly) used for developing website$ and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a  of everyday tasks, like organizing finances."

# remove user defined unnecessary phrases from dataframe
import pandas as pd
original_data = pd.DataFrame({'string1': ['abcstack overflow','abc123','comedy*','definitely$','lkjh','pls1234'],
                      'string2': ['1!', '2a', '3cft', 'google*', 'microsoft)', 'yahoo]']})
remove_words = ['abc', 'deff', 'pls', '\*', '\@', '\$', '\)', '\]', '\!']

filtered_data = keytxt.dataframe_keyword_remover(remove_words, original_data, '')


print('original_data:\n', original_data)
print('\n\n')
print('after passing filter:\n', filtered_data)

original_data:
              string1     string2
0  abcstack overflow          1!
1             abc123          2a
2            comedy*        3cft
3        definitely$     google*
4               lkjh  microsoft)
5            pls1234      yahoo]



after passing filter:
           string1    string2
0  stack overflow          1
1             123         2a
2          comedy       3cft
3      definitely     google
4            lkjh  microsoft
5            1234      yahoo

Change Log

0.0.1 (24/01/2022) - First Release 0.0.2 (30/01/2022) - Second Release 0.0.3 (19/02/2022) - Third Release