NLP Data Preparation: From Regex to Word Cloud Packages and Data Visualization

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Mar 11, 2021
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NLP Data Preparation: From Regex to Word Cloud Packages and Data Visualization

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value<p>“REGEX‘’ and “Word Clouds” for Natural Language Processing (NLP) data preparation? `Yesss! Regex, short for “the regular expression”, is not an old technique to find and extract text data. It is still one of the basic techniques used in scraping or even in hot AI topics like NLP. Also, word clouds are still used in the initial stage of almost all NLP projects.</p>
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“REGEX‘’ and “Word Clouds” for Natural Language Processing (NLP) data preparation? `Yesss! Regex, short for “the regular expression”, is not an old technique to find and extract text data. It is still one of the basic techniques used in scraping or even in hot AI topics like NLP. Also, word clouds are still used in the initial stage of almost all NLP projects.

Execution time: 0.0015 seconds

Execution time: 0.0003 seconds

Regular expressions are extremely useful in extracting information from any text by searching for one or more matches of a specific pattern.

Fields of applications range from validation to parsing/replacing strings, to translating data to other formats, NLP, and web scraping.

Visualization is also one of the very important steps of all Machine Learning projects and we will cover this and some analyses in the tutorial.

Here is a list of the topics covered and an interesting project, which we will build step by step.

Curious?

Let´s start with the concepts and build the final project in the six steps listed below:

  • Scraping news articles on “AI” from Google news
  • Cleaning the data using regex and generate word cloud on clean text
  • Visualization of the unigram, bigram, and trigram on the text data
  • Parts of speech tagging
  • Named entity recognition
  • Sentiment analysis

1. Scraping news articles on “AI” from Google news

If we want to scrape articles from Google news, there are a few parameters that we can use to build a search query.

All Google search URLs start with https://www.google.com/search?

# Build a query
topic=”AI”
numResults=1000
url ="https://www.google.com/search?q="+topic+"&tbm=nws&hl=en&num="+str(numResults)

q — this is the query topic, i.e., q=AI if you’re searching for Artificial Intelligence news

hl — the interface language, i.e., hl=en for English

tbm — to be matched, here we need tbm=nws to search for news items.

There’s a whole lot of other things one can match.

For instance, app for applications, blg for blogs, bks for books, isch for images, plcs for places, vid for videos, shop for shopping, and rcp for recipes.

num — controls the number of results shown. If you only want 10 results shown, num=10

To scrape 1000 articles, clean and visualize them using a word cloud.

Start scraping the articles using the URL we built above.

response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser’)
Web scraping resources – Source: octaparse

Web scraping resources – Source: octaparse

2. Cleaning the data using regex & generating a word cloud

Now we have all the articles but they are in HTML format, which we have to clean and transform in a format that we can analyze and visualize through a word cloud.

Before we will revise a few of the regex concepts. We know that regular expressions are useful to replace or remove characters.

1. Remove the brackets then use regex

re.sub(‘[\\(\\[].*?[\\)\\]]’, ‘ ‘, text)

2. Remove extra spaces

re.sub(“s+”,” “, doc)

3. Remove punctuations

re.sub(“[^-9A-Za-z ]”, “” , text)

4. Convert data in lower or upper

text.lower()
text.upper()

5. Remove special characters using regex

pattern = r’[^a-zA-Z0–9\\s]’ if not remove_digits else r’[^a-zA-Z\\s]’ re.sub(pattern, ‘’, text)

6. Stopwords include: I, he, she, and, but, was were, being, have, etc, which do not add meaning to the data. So these words (tokenizing the text) must be removed which helps to reduce the features from our data.

stopword_list = stopwords.words(‘english’)
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
‘ ‘.join([token for token in tokens if token not in stopword_list])

Let’s start cleaning our downloaded data!

# we are finding all data under division tags with class ZINbbc
results = soup.find_all(“div”, attrs = {“class”: “ZINbbc”})
# we will take out all data from description from class s3v9rd
descriptions = []
for result in results:
    try:
       description = result.find(“div”, attrs={“class”:”s3v9rd”}).get_text()
       if description != “”:
            descriptions.append(description)
    except:
        continue
# Join all text into one to start cleaning them together for making word cloud
text = “”.join(descriptions)

When we print the variable text it’s like:

Google scraping output for the word “AI” – Source: Omdena, tutorials

Google scraping output for the word “AI” – Source: Omdena, tutorials

To generate the word cloud and see the most famous words related to AI articles.

# To generate the wordcloud we can use this code
wordcloud = WordCloud(stopwords=STOPWORDS).generate(text_clean)
plt.imshow(wordcloud, interpolation=”bilinear”)
plt.axis(“off”)
plt.show()
This is a word cloud generated from the AI articles collected - Source: Omdena

This is a word cloud generated from the AI articles collected – Source: Omdena

Let’s try a different cleaning method to generate a word cloud on the same text!

Here we will choose only nouns and make every text in lowercase as done before

import nltk
nltk.download(‘averaged_perceptron_tagger’)
# function to test if something is a noun
is_noun = lambda pos: pos[:2] == ‘NN’
# do the nlp stuff
tokenized = nltk.word_tokenize(text)
nouns = [word.lower() for (word, pos) in nltk.pos_tag(tokenized) if is_noun(pos)]
# Joining them all
text_noun = “ “.join(nouns)
# Then print the wordcloud
wordcloud = WordCloud(stopwords=STOPWORDS).generate(text_noun)
plt.imshow(wordcloud, interpolation=”bilinear”)
plt.axis(“off”)
plt.show()
This is a word cloud generated from the AI articles collected using only nouns – Source: Omdena

This is a word cloud generated from the AI articles collected using only nouns – Source: Omdena

But what if we add some more stop words and make this word cloud more meaningful?

# By adding some more stops words to the list
wordcloud = WordCloud(stopwords=set(list(STOPWORDS)+[‘day’,’ai’,’ago’,’hour’,’hours’,’days’])).generate(text_noun)
plt.imshow(wordcloud, interpolation=”bilinear”)
plt.axis(“off”)
plt.show()
This is a word cloud generated from the AI articles collected and after custom filtering words – Source: Omdena

This is a word cloud generated from the AI articles collected and after custom filtering words – Source: Omdena

The word cloud is more meaningful now.

3. Visualizing the unigram, bigram, and trigram on the text data

To visualize the n-grams. We will start extracting N-Gram features and see their distribution. N-Gram is used to describe the number of words used as observation points, e.g., unigram means singly-worded, bigram means the 2-worded phrase, and trigram means 3-worded phrase. This helps us to understand which words most occur together and make our text cleaner and understanding the text distribution.

# Visualizing unigrams
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
from plotly.offline import iplot
def get_top_n_words(corpus, n=None):
    vec = CountVectorizer(stop_words = ‘english’).fit(corpus)
    bag_of_words = vec.transform(corpus)
    sum_words = bag_of_words.sum(axis=0)
    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
    return words_freq[:n]

common_words = get_top_n_words(descriptions, 20)
for word, freq in common_words:
    print(word, freq)

df2 = pd.DataFrame(common_words, columns = [‘ReviewText’ , ‘count’])
df2.groupby(‘ReviewText’).sum()[‘count’].sort_values(ascending=False).plot(kind=’bar’)

As we have seen earlier in the word cloud; day, ai, etc are the most occurring words. We can see the most occurring words are highlighted in bold in the word cloud.

Now, we will try visualizing bigrams, which means the two most simultaneously occurring words.

def get_top_n_bigram(corpus, n=None):
    vec = CountVectorizer(ngram_range=(2, 2), stop_words=’english’).fit(corpus)
    bag_of_words = vec.transform(corpus)
    sum_words = bag_of_words.sum(axis=0)
    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
    return words_freq[:n]

common_words = get_top_n_bigram(descriptions, 20)
for word, freq in common_words:
    print(word, freq)

df4 = pd.DataFrame(common_words, columns = [‘ReviewText’ , ‘count’])
df4.groupby(‘ReviewText’).sum()[‘count’].sort_values(ascending=False).plot(kind=’bar’)

The same applies to trigrams.

# Visualizing trigrams
def get_top_n_bigram(corpus, n=None):
    vec = CountVectorizer(ngram_range=(3, 3), stop_words=’english’).fit(corpus)
    bag_of_words = vec.transform(corpus)
    sum_words = bag_of_words.sum(axis=0)
    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
    return words_freq[:n]

common_words = get_top_n_bigram(descriptions, 20)
for word, freq in common_words:
    print(word, freq)

df4 = pd.DataFrame(common_words, columns = [‘ReviewText’ , ‘count’])
df4.groupby(‘ReviewText’).sum()[‘count’].sort_values(ascending=False).plot(kind=’bar’)

This analysis helps us to understand which words are most repeated like “days ago”, “ai”, “12 hours ago”, etc which won’t help us to understand the data better or build a better model, so it’s better to drop them.

4. Applying parts of speech tagging distribution

Let us do parts of speech tagging and see which parts of speech are most occurring in the text corpus.

from textblob import TextBlob
blob = TextBlob(str(descriptions))
pos_df = pd.DataFrame(blob.tags, columns = [‘word’ , ‘pos’])
pos_df = pos_df.pos.value_counts()[:20]
pos_df.plot(kind=’bar’)

5. Using Named Entity Recognition

Named Entity Recognition (NER) is a standard NLP problem that involves spotting named entities (people, places, organizations, etc.) from a chunk of text, and classifying them into a predefined set of categories.

Name entity from splunk.com

Name entity from splunk.com

When we use this NER our code can understand which word means a person or organization etc.

nltk.download(‘words’)
nltk.download(‘maxent_ne_chunker’)
ne_tree = nltk.ne_chunk(pos_tag(word_tokenize(text_clean)))
print(ne_tree)

This gives an output of a tree that helps us understand the NER structure and algorithm of our corpus.

Mostly the output will be in form of IOB tags

What do these IOB tags mean?

B-NP: the beginning of a noun phrase

I-NP: describes that the word is inside of the current noun phrase.

O: end of the sentence.

B-VP and I-VP: beginning and inside of a verb phrase.

6. Applying Sentiment Analysis

Sentiment analysis from translatemedia.com

Sentiment analysis from translatemedia.com

Let us try to use textblob to get the sentiment from the description of the AI articles.

from textblob import TextBlob
# compute sentiment scores (polarity) and labels
sentiment_scores_tb = [round(TextBlob(article).sentiment.polarity, 3) for article in descriptions]
sentiment_category_tb = [‘positive’ if score > 0
                        else ‘negative’ if score < 0
                        else ‘neutral’
                        for score in sentiment_scores_tb]

# We don’t have any labels to compare the performance of our model but we can definitely check it ourselves.
# Let us save all these in a data frame and check the model output

sentiment=pd.DataFrame()
sentiment[‘article’]=descriptions
sentiment[‘sentiments’]=sentiment_category_tb

We can see that our model is performing fairly well as if you read the 4th index description, which states “AI rising” kind words to be some threat kind sentiment.

We can also try models like Affin, these tools are useful when you don’t have any training data but want to quickly try out some ML models.

The code is available as a Jupyter Notebook here.

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background_enable_video_webmon
allow_player_pauseoff
background_video_pause_outside_viewporton
background_enable_pattern_styleoff
background_pattern_stylepolka-dots
background_pattern_colorrgba(0,0,0,0.2)
background_pattern_sizeinitial
background_pattern_widthauto
background_pattern_heightauto
background_pattern_repeat_origintop_left
background_pattern_horizontal_offset0
background_pattern_vertical_offset0
background_pattern_repeatrepeat
background_pattern_blend_modenormal
background_enable_mask_styleoff
background_mask_stylelayer-blob
background_mask_color#ffffff
background_mask_aspect_ratiolandscape
background_mask_sizestretch
background_mask_widthauto
background_mask_heightauto
background_mask_positioncenter
background_mask_horizontal_offset0
background_mask_vertical_offset0
background_mask_blend_modenormal
custom_buttonoff
button_text_size20
button_bg_use_color_gradientoff
button_bg_color_gradient_repeatoff
button_bg_color_gradient_typelinear
button_bg_color_gradient_direction180deg
button_bg_color_gradient_direction_radialcenter
button_bg_color_gradient_stops#2b87da 0%|#29c4a9 100%
button_bg_color_gradient_unit%
button_bg_color_gradient_overlays_imageoff
button_bg_color_gradient_start#2b87da
button_bg_color_gradient_start_position0%
button_bg_color_gradient_end#29c4a9
button_bg_color_gradient_end_position100%
button_bg_enable_imageon
button_bg_parallaxoff
button_bg_parallax_methodon
button_bg_sizecover
button_bg_image_widthauto
button_bg_image_heightauto
button_bg_positioncenter
button_bg_horizontal_offset0
button_bg_vertical_offset0
button_bg_repeatno-repeat
button_bg_blendnormal
button_bg_enable_video_mp4on
button_bg_enable_video_webmon
button_bg_allow_player_pauseoff
button_bg_video_pause_outside_viewporton
button_use_iconon
button_icon_placementright
button_on_hoveron
positioningnone
position_origin_atop_left
position_origin_ftop_left
position_origin_rtop_left
width100%
max_widthnone
max_width_tablet25%
max_width_phone25%
max_width_last_editedon|tablet
module_alignmentcenter
min_heightauto
heightauto
max_heightnone
custom_margin_tablet||0px||false|false
custom_margin_phone||0px||false|false
custom_margin_last_editedon|phone
filter_hue_rotate0deg
filter_saturate100%
filter_brightness100%
filter_contrast100%
filter_invert0%
filter_sepia0%
filter_opacity100%
filter_blur0px
mix_blend_modenormal
animation_stylenone
animation_directioncenter
animation_duration1000ms
animation_delay0ms
animation_intensity_slide50%
animation_intensity_zoom50%
animation_intensity_flip50%
animation_intensity_fold50%
animation_intensity_roll50%
animation_starting_opacity0%
animation_speed_curveease-in-out
animation_repeatonce
hover_transition_duration300ms
hover_transition_delay0ms
hover_transition_speed_curveease
link_option_url_new_windowoff
sticky_positionnone
sticky_offset_top0px
sticky_offset_bottom0px
sticky_limit_topnone
sticky_limit_bottomnone
sticky_offset_surroundingon
sticky_transitionon
motion_trigger_startmiddle
hover_enabled0
title_css_text_shadow_stylenone
title_css_text_shadow_horizontal_length0em
title_css_text_shadow_vertical_length0em
title_css_text_shadow_blur_strength0em
title_css_text_shadow_colorrgba(0,0,0,0.4)
acf_label_css_text_shadow_stylenone
acf_label_css_text_shadow_horizontal_length0em
acf_label_css_text_shadow_vertical_length0em
acf_label_css_text_shadow_blur_strength0em
acf_label_css_text_shadow_colorrgba(0,0,0,0.4)
label_css_text_shadow_stylenone
label_css_text_shadow_horizontal_length0em
label_css_text_shadow_vertical_length0em
label_css_text_shadow_blur_strength0em
label_css_text_shadow_colorrgba(0,0,0,0.4)
text_before_css_text_shadow_stylenone
text_before_css_text_shadow_horizontal_length0em
text_before_css_text_shadow_vertical_length0em
text_before_css_text_shadow_blur_strength0em
text_before_css_text_shadow_colorrgba(0,0,0,0.4)
seperator_text_shadow_stylenone
seperator_text_shadow_horizontal_length0em
seperator_text_shadow_vertical_length0em
seperator_text_shadow_blur_strength0em
seperator_text_shadow_colorrgba(0,0,0,0.4)
relational_field_item_text_shadow_stylenone
relational_field_item_text_shadow_horizontal_length0em
relational_field_item_text_shadow_vertical_length0em
relational_field_item_text_shadow_blur_strength0em
relational_field_item_text_shadow_colorrgba(0,0,0,0.4)
border_radiion|100%|100%|100%|100%
border_radii_tableton||||
border_radii_phoneon|100%|100%|100%|100%
border_radii_last_editedon|phone
button_text_shadow_stylenone
button_text_shadow_horizontal_length0em
button_text_shadow_vertical_length0em
button_text_shadow_blur_strength0em
button_text_shadow_colorrgba(0,0,0,0.4)
box_shadow_stylenone
box_shadow_colorrgba(0,0,0,0.3)
box_shadow_positionouter
box_shadow_style_buttonnone
box_shadow_color_buttonrgba(0,0,0,0.3)
box_shadow_position_buttonouter
text_shadow_stylenone
text_shadow_horizontal_length0em
text_shadow_vertical_length0em
text_shadow_blur_strength0em
text_shadow_colorrgba(0,0,0,0.4)
disabledoff
global_colors_info{}
Profile

Execution time: 0.0058 seconds

ACF

ID58156
keyfield_623341caec7cf
labelName
nameblog_author_name
prefixacf
typetext
valueRinki Nag
parent58155
wrapperArray
_nameblog_author_name
_valid1

Module Settings

custom_identifierACF Item
acf_namefield_623341caec7cf
is_author_acf_fieldoff
post_object_acf_namenone
author_field_typeauthor_post
linked_user_acf_namenone
type_taxonomy_acf_namenone
acf_tagp
show_labeloff
label_seperator:
visibilityon
empty_value_optionhide_module
use_iconoff
icon_color#7EBEC5
use_circleoff
circle_color#7EBEC5
use_circle_borderoff
circle_border_color#7EBEC5
use_icon_font_sizeoff
icon_image_placementleft
image_mobile_stackinginitial
return_formatarray
image_link_urloff
image_link_url_acf_namenone
checkbox_stylearray
checkbox_radio_returnlabel
checkbox_radio_value_typeoff
checkbox_radio_linkoff
link_buttonoff
email_subjectnone
email_body_afternone
add_css_classoff
add_css_loop_layoutoff
add_css_class_selectorbody
link_new_tabon
link_name_acfoff
link_name_acf_namenone
url_link_iconoff
image_sizefull
true_false_conditionoff
true_false_condition_css_selector.et_pb_button
true_false_text_trueTrue
true_false_text_falseFalse
is_audiooff
is_videooff
video_loopon
video_autoplayon
is_oembed_videooff
defer_videooff
defer_video_iconI||divi||400
video_icon_font_sizeoff
pretify_textoff
pretify_seperator,
number_decimal.
show_value_if_zerooff
text_imageoff
is_options_pageoff
is_repeater_loop_layoutoff
linked_post_stylecustom
link_post_seperator,
link_to_post_objecton
loop_layoutnone
columns4
columns_tablet2
columns_mobile1
repeater_dyn_btn_acfnone
text_before_positionsame_line
label_positionsame_line
vertical_alignmentmiddle
admin_labelName
_builder_version4.21.0
_module_presetdefault
title_css_text_alignleft
title_css_font_size14px
title_css_letter_spacing0px
title_css_line_height1em
acf_label_css_text_alignleft
acf_label_css_font_size14px
acf_label_css_letter_spacing0px
acf_label_css_line_height1em
label_css_fontRoboto|700|||||||
label_css_text_alignleft
label_css_letter_spacing0px
text_before_css_font_size14px
text_before_css_letter_spacing0px
text_before_css_line_height1em
seperator_font_size14px
seperator_letter_spacing0px
seperator_line_height1em
relational_field_item_font_size14px
relational_field_item_letter_spacing0px
relational_field_item_line_height1em
background_enable_coloron
use_background_color_gradientoff
background_color_gradient_repeatoff
background_color_gradient_typelinear
background_color_gradient_direction180deg
background_color_gradient_direction_radialcenter
background_color_gradient_stops#2b87da 0%|#29c4a9 100%
background_color_gradient_unit%
background_color_gradient_overlays_imageoff
background_color_gradient_start#2b87da
background_color_gradient_start_position0%
background_color_gradient_end#29c4a9
background_color_gradient_end_position100%
background_enable_imageon
parallaxoff
parallax_methodon
background_sizecover
background_image_widthauto
background_image_heightauto
background_positioncenter
background_horizontal_offset0
background_vertical_offset0
background_repeatno-repeat
background_blendnormal
background_enable_video_mp4on
background_enable_video_webmon
allow_player_pauseoff
background_video_pause_outside_viewporton
background_enable_pattern_styleoff
background_pattern_stylepolka-dots
background_pattern_colorrgba(0,0,0,0.2)
background_pattern_sizeinitial
background_pattern_widthauto
background_pattern_heightauto
background_pattern_repeat_origintop_left
background_pattern_horizontal_offset0
background_pattern_vertical_offset0
background_pattern_repeatrepeat
background_pattern_blend_modenormal
background_enable_mask_styleoff
background_mask_stylelayer-blob
background_mask_color#ffffff
background_mask_aspect_ratiolandscape
background_mask_sizestretch
background_mask_widthauto
background_mask_heightauto
background_mask_positioncenter
background_mask_horizontal_offset0
background_mask_vertical_offset0
background_mask_blend_modenormal
custom_buttonoff
button_text_size20
button_bg_use_color_gradientoff
button_bg_color_gradient_repeatoff
button_bg_color_gradient_typelinear
button_bg_color_gradient_direction180deg
button_bg_color_gradient_direction_radialcenter
button_bg_color_gradient_stops#2b87da 0%|#29c4a9 100%
button_bg_color_gradient_unit%
button_bg_color_gradient_overlays_imageoff
button_bg_color_gradient_start#2b87da
button_bg_color_gradient_start_position0%
button_bg_color_gradient_end#29c4a9
button_bg_color_gradient_end_position100%
button_bg_enable_imageon
button_bg_parallaxoff
button_bg_parallax_methodon
button_bg_sizecover
button_bg_image_widthauto
button_bg_image_heightauto
button_bg_positioncenter
button_bg_horizontal_offset0
button_bg_vertical_offset0
button_bg_repeatno-repeat
button_bg_blendnormal
button_bg_enable_video_mp4on
button_bg_enable_video_webmon
button_bg_allow_player_pauseoff
button_bg_video_pause_outside_viewporton
button_use_iconon
button_icon_placementright
button_on_hoveron
positioningnone
position_origin_atop_left
position_origin_ftop_left
position_origin_rtop_left
text_orientationleft
widthauto
max_widthnone
module_alignmentleft
min_heightauto
heightauto
max_heightnone
custom_margin_tablet||10px||false|false
custom_margin_phone||10px||false|false
custom_margin_last_editedon|tablet
custom_padding5px||||false|false
filter_hue_rotate0deg
filter_saturate100%
filter_brightness100%
filter_contrast100%
filter_invert0%
filter_sepia0%
filter_opacity100%
filter_blur0px
mix_blend_modenormal
animation_stylenone
animation_directioncenter
animation_duration1000ms
animation_delay0ms
animation_intensity_slide50%
animation_intensity_zoom50%
animation_intensity_flip50%
animation_intensity_fold50%
animation_intensity_roll50%
animation_starting_opacity0%
animation_speed_curveease-in-out
animation_repeatonce
hover_transition_duration300ms
hover_transition_delay0ms
hover_transition_speed_curveease
link_option_url_new_windowoff
sticky_positionnone
sticky_offset_top0px
sticky_offset_bottom0px
sticky_limit_topnone
sticky_limit_bottomnone
sticky_offset_surroundingon
sticky_transitionon
motion_trigger_startmiddle
hover_enabled0
title_css_text_align_tabletcenter
title_css_text_align_phonecenter
title_css_text_align_last_editedon|phone
acf_label_css_text_align_tabletcenter
acf_label_css_text_align_phonecenter
acf_label_css_text_align_last_editedon|phone
label_css_text_align_tabletcenter
label_css_text_align_phonecenter
label_css_text_align_last_editedon|desktop
text_orientation_tabletcenter
text_orientation_phonecenter
text_orientation_last_editedon|phone
module_alignment_tabletcenter
module_alignment_phonecenter
module_alignment_last_editedon|desktop
title_css_text_shadow_stylenone
title_css_text_shadow_horizontal_length0em
title_css_text_shadow_vertical_length0em
title_css_text_shadow_blur_strength0em
title_css_text_shadow_colorrgba(0,0,0,0.4)
acf_label_css_text_shadow_stylenone
acf_label_css_text_shadow_horizontal_length0em
acf_label_css_text_shadow_vertical_length0em
acf_label_css_text_shadow_blur_strength0em
acf_label_css_text_shadow_colorrgba(0,0,0,0.4)
label_css_text_shadow_stylenone
label_css_text_shadow_horizontal_length0em
label_css_text_shadow_vertical_length0em
label_css_text_shadow_blur_strength0em
label_css_text_shadow_colorrgba(0,0,0,0.4)
text_before_css_text_shadow_stylenone
text_before_css_text_shadow_horizontal_length0em
text_before_css_text_shadow_vertical_length0em
text_before_css_text_shadow_blur_strength0em
text_before_css_text_shadow_colorrgba(0,0,0,0.4)
seperator_text_shadow_stylenone
seperator_text_shadow_horizontal_length0em
seperator_text_shadow_vertical_length0em
seperator_text_shadow_blur_strength0em
seperator_text_shadow_colorrgba(0,0,0,0.4)
relational_field_item_text_shadow_stylenone
relational_field_item_text_shadow_horizontal_length0em
relational_field_item_text_shadow_vertical_length0em
relational_field_item_text_shadow_blur_strength0em
relational_field_item_text_shadow_colorrgba(0,0,0,0.4)
button_text_shadow_stylenone
button_text_shadow_horizontal_length0em
button_text_shadow_vertical_length0em
button_text_shadow_blur_strength0em
button_text_shadow_colorrgba(0,0,0,0.4)
box_shadow_stylenone
box_shadow_colorrgba(0,0,0,0.3)
box_shadow_positionouter
box_shadow_style_buttonnone
box_shadow_color_buttonrgba(0,0,0,0.3)
box_shadow_position_buttonouter
text_shadow_stylenone
text_shadow_horizontal_length0em
text_shadow_vertical_length0em
text_shadow_blur_strength0em
text_shadow_colorrgba(0,0,0,0.4)
disabledoff
global_colors_info{}

Rinki Nag

Execution time: 0.0011 seconds

Execution time: 0.0003 seconds

Execution time: 0.0005 seconds

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