Example usage

Here we will demonstrate how to use clevercloud in a project to create a meaningful word cloud:

Imports

from clevercloud.CleverClean import CleverClean
from clevercloud.CleverLemStem import CleverLemStem
from clevercloud.CleverStopwords import CleverStopwords
from clevercloud.CleverWordCloud import CleverWordCloud

Create a Pandas series

We will first create a Pandas series to use for CleverClean:

import pandas as pd

text = ["is is a feet feet crying beautiful123", "maximum feet RUNNING!!", "BEAUTIFUL feet beautiful crying"]
test_text = pd.Series(text)
test_text

  is is a feet feet crying beautiful123
                 maximum feet RUNNING!!
        BEAUTIFUL feet beautiful crying
dtype: object

CleverClean

CleverClean is a preprocessor to convert all the letters to lower case and remove punctuations.

clean_text = CleverClean(test_text)
clean_text

'is is a feet feet crying beautiful maximum feet running beautiful feet beautiful crying '

CleverLemStem

CleverLemStem is a preprocessor to conduct lemmatization and stemming on the text.

final_text = CleverLemStem(clean_text)
final_text

[nltk_data] Downloading package omw-1.4 to /home/docs/nltk_data...

[nltk_data]   Unzipping corpora/omw-1.4.zip.

[nltk_data] Downloading package wordnet to /home/docs/nltk_data...
[nltk_data]   Unzipping corpora/wordnet.zip.

'is is a foot foot cry beauty maxim foot run beauty foot beauty cry'

CleverStopwords

CleverStopwords is a comprehensive list of English stopwords that allow adding more customized words.

new_stopwords = CleverStopwords({"foot", "cry"})
new_stopwords

[nltk_data] Downloading package stopwords to /home/docs/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package stopwords to /home/docs/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

{'a',
 'about',
 'above',
 'after',
 'again',
 'against',
 'ain',
 'all',
 'am',
 'an',
 'and',
 'any',
 'are',
 'aren',
 "aren't",
 'as',
 'at',
 'be',
 'because',
 'been',
 'before',
 'being',
 'below',
 'between',
 'both',
 'but',
 'by',
 'can',
 'couldn',
 "couldn't",
 'cry',
 'd',
 'did',
 'didn',
 "didn't",
 'do',
 'does',
 'doesn',
 "doesn't",
 'doing',
 'don',
 "don't",
 'down',
 'during',
 'each',
 'few',
 'foot',
 'for',
 'from',
 'further',
 'had',
 'hadn',
 "hadn't",
 'has',
 'hasn',
 "hasn't",
 'have',
 'haven',
 "haven't",
 'having',
 'he',
 'her',
 'here',
 'hers',
 'herself',
 'him',
 'himself',
 'his',
 'how',
 'i',
 'if',
 'in',
 'into',
 'is',
 'isn',
 "isn't",
 'it',
 "it's",
 'its',
 'itself',
 'just',
 'll',
 'm',
 'ma',
 'me',
 'mightn',
 "mightn't",
 'more',
 'most',
 'mustn',
 "mustn't",
 'my',
 'myself',
 'needn',
 "needn't",
 'no',
 'nor',
 'not',
 'now',
 'o',
 'of',
 'off',
 'on',
 'once',
 'only',
 'or',
 'other',
 'our',
 'ours',
 'ourselves',
 'out',
 'over',
 'own',
 're',
 's',
 'same',
 'shan',
 "shan't",
 'she',
 "she's",
 'should',
 "should've",
 'shouldn',
 "shouldn't",
 'so',
 'some',
 'such',
 't',
 'than',
 'that',
 "that'll",
 'the',
 'their',
 'theirs',
 'them',
 'themselves',
 'then',
 'there',
 'these',
 'they',
 'this',
 'those',
 'through',
 'to',
 'too',
 'under',
 'until',
 'up',
 've',
 'very',
 'was',
 'wasn',
 "wasn't",
 'we',
 'were',
 'weren',
 "weren't",
 'what',
 'when',
 'where',
 'which',
 'while',
 'who',
 'whom',
 'why',
 'will',
 'with',
 'won',
 "won't",
 'wouldn',
 "wouldn't",
 'y',
 'you',
 "you'd",
 "you'll",
 "you're",
 "you've",
 'your',
 'yours',
 'yourself',
 'yourselves'}

CleverWordCloud

CleverWordCloud is a function to generate a meaningful word cloud that allows customized stopwords.

image = CleverWordCloud(final_text, new_stopwords, 3)
image

Matplotlib is building the font cache; this may take a moment.