Compare Vocabulary Differences Between Ranking Web Pages On SERP With Python

Vocabulary measurement and distinction are semantic and linguistic ideas for mathematical and qualitative linguistics.

For instance, Heaps’ legislation claims that the size of the article and vocabulary measurement are correlative. Nonetheless, after a sure threshold, the identical phrases proceed to look with out enhancing vocabulary measurement.

The Word2Vec makes use of Steady Bag of Phrases (CBOW) and Skip-gram to know the regionally contextually related phrases and their distance to one another. On the identical time, GloVe tries to make use of matrix factorization with context windows.

Zipf’s legislation is a complementary concept to Heaps’ legislation. It states that essentially the most frequent and second most frequent phrases have an everyday proportion distinction between them.

There are different distributional semantics and linguistic theories in statistical natural language processing.

However “vocabulary comparability” is a basic methodology for search engines like google and yahoo to know “topicality variations,” “the principle subject of the doc,” or general “experience of the doc.”

Paul Haahr of Google said that it compares the “question vocabulary” to the “doc vocabulary.”

David C. Taylor and his designs for context domains contain sure phrase vectors in vector search to see which doc and which doc subsection are extra about what, so a search engine can rank and rerank paperwork primarily based on search question modifications.

Evaluating vocabulary variations between rating internet pages on the search engine outcomes web page (SERP) helps SEO execs see what contexts, concurrent phrases, and phrase proximity they’re skipping in comparison with their opponents.

It’s useful to see context variations within the paperwork.

On this information, the Python programming language is used to go looking on Google and take SERP objects (snippets) to crawl their content material, tokenize and examine their vocabulary to one another.

How To Examine Rating Internet Paperwork’ Vocabulary With Python?

To check the vocabularies of rating internet paperwork (with Python), the used libraries and packages of Python programming language are listed beneath.

  • Googlesearch is a Python bundle for performing a Google search with a question, area, language, variety of outcomes, request frequency, or secure search filters.
  • URLlib is a Python library for parsing the URLs to the netloc, scheme, or path.
  • Requests (non-obligatory) are to take the titles, descriptions, and hyperlinks on the SERP objects (snippets).
  • Fake_useragent is a Python bundle to make use of pretend and random person brokers to forestall 429 standing codes.
  • Advertools is used to crawl the URLs on the Google question search outcomes to take their physique textual content for textual content cleansing and processing.
  • Pandas regulate and mixture the info for additional evaluation of the distributional semantics of paperwork on the SERP.
  • Pure LanguageTool equipment is used to tokenize the content material of the paperwork and use English cease phrases for cease phrase removing.
  • Collections to make use of the “Counter” technique for counting the prevalence of the phrases.
  • The string is a Python module that calls all punctuation in an inventory for punctuation character cleansing.

What Are The Steps For Comparability Of Vocabulary Sizes, And Content material Between Internet Pages?

The steps for evaluating the vocabulary measurement and content material between rating internet pages are listed beneath.

  • Import the required Python libraries and packages for retrieving and processing the textual content content material of internet pages.
  • Carry out a Google search to retrieve the consequence URLs on the SERP.
  • Crawl the URLs to retrieve their physique textual content, which comprises their content material.
  • Tokenize content material of the online pages for textual content processing in NLP methodologies.
  • Take away the cease phrases and the punctuation for higher clear textual content evaluation.
  • Rely the variety of phrases occurrences within the internet web page’s content material.
  • Assemble a Pandas Knowledge body for additional and higher textual content evaluation.
  • Select two URLs, and examine their phrase frequencies.
  • Examine the chosen URL’s vocabulary measurement and content material.

1. Import The Vital Python Libraries And Packages For Retrieving And Processing The Textual content Content material Of Internet Pages

Import the required Python libraries and packages through the use of the “from” and “import” instructions and strategies.

from googlesearch import search

from urllib.parse import urlparse

import requests

from fake_useragent import UserAgent

import advertools as adv

import pandas as pd

from nltk.tokenize import word_tokenize

import nltk

from collections import Counter

from nltk.corpus import stopwords

import string

nltk.obtain()

Use the “nltk.obtain” provided that you’re utilizing NLTK for the primary time. Obtain all of the corpora, fashions, and packages. It can open a window as beneath.

NLTK downloaderScreenshot from creator, August 2022

Refresh the window now and again; if every little thing is inexperienced, shut the window in order that the code working in your code editor stops and completes.

In case you don’t have some modules above, use the “pip set up” technique for downloading them to your native machine. In case you have a closed-environment mission, use a digital surroundings in Python.

2. Carry out A Google Search To Retrieve The Outcome URLs On The Search Engine Outcome Pages

To carry out a Google search to retrieve the consequence URLs on the SERP objects, use a for loop within the “search” object, which comes from “Googlesearch” bundle.

serp_item_url = []

for i in search("search engine marketing", num=10, begin=1, cease=10, pause=1, lang="en", nation="us"):

    serp_item_url.append(i)

    print(i)

The reason of the code block above is:

  • Create an empty record object, comparable to “serp_item_url.”
  • Begin a for loop inside the “search” object that states a question, language, variety of outcomes, first and final consequence, and nation restriction.
  • Append all the outcomes to the “serp_item_url” object, which entails a Python record.
  • Print all of the URLs that you’ve retrieved from Google SERP.

You may see the consequence beneath.

The rating URLs for the question “search engine marketing” is given above.

The following step is parsing these URLs for additional cleansing.

As a result of if the outcomes contain “video content material,” it gained’t be potential to carry out a wholesome textual content evaluation if they don’t have an extended video description or too many feedback, which is a distinct content material kind.

3. Clear The Video Content material URLs From The Outcome Internet Pages

To wash the video content material URLs, use the code block beneath.

parsed_urls = []


for i in vary(len(serp_item_url)):

    parsed_url = urlparse(serp_item_url[i])

    i += 1

    full_url = parsed_url.scheme + '://' + parsed_url.netloc + parsed_url.path


    if ('youtube' not in full_url and 'vimeo' not in full_url and 'dailymotion' not in full_url and "dtube" not in full_url and "sproutvideo" not in full_url and "wistia" not in full_url):

        parsed_urls.append(full_url)

The video search engines like google and yahoo comparable to YouTube, Vimeo, Dailymotion, Sproutvideo, Dtube, and Wistia are cleaned from the ensuing URLs if they seem within the outcomes.

You should utilize the identical cleansing methodology for the web sites that you just suppose will dilute the effectivity of your evaluation or break the outcomes with their very own content material kind.

For instance, Pinterest or different visual-heavy web sites may not be essential to verify the “vocabulary measurement” variations between competing paperwork.

Clarification of code block above:

  • Create an object comparable to “parsed_urls.”
  • Create a for loop within the vary of size of the retrieved consequence URL depend.
  • Parse the URLs with “urlparse” from “URLlib.”
  • Iterate by growing the depend of “i.”
  • Retrieve the complete URL by uniting the “scheme”, “netloc”, and “path.”
  • Carry out a search with situations within the “if” assertion with “and” situations for the domains to be cleaned.
  • Take them into an inventory with “dict.fromkeys” technique.
  • Print the URLs to be examined.

You may see the consequence beneath.

Video Content URLsScreenshot from creator, August 2022

4. Crawl The Cleaned Study URLs For Retrieving Their Content material

Crawl the cleaned look at URLs for retrieving their content material with advertools.

It’s also possible to use requests with a for loop and record append methodology, however advertools is quicker for crawling and creating the info body with the ensuing output.

With requests, you manually retrieve and unite all of the “p” and “heading” parts.

adv.crawl(examine_urls, output_file="examine_urls.jl",

          follow_links=False,

          custom_settings={"USER_AGENT": UserAgent().random,

                           "LOG_FILE": "examine_urls.log",

                           "CRAWL_DELAY": 2})

crawled_df = pd.read_json("examine_urls.jl", traces=True)

crawled_df

Clarification of code block above:

  • Use “adv.crawl” for crawling the “examine_urls” object.
  • Create a path for output recordsdata with “jl” extension, which is smaller than others.
  • Use “follow_links=false” to cease crawling just for listed URLs.
  • Use custom settings to state a “random person agent” and a crawl log file if some URLs don’t reply the crawl requests. Use a crawl delay configuration to forestall 429 standing code chance.
  • Use pandas “read_json” with the “traces=True” parameter to learn the outcomes.
  • Name the “crawled_df” as beneath.

You may see the consequence beneath.

Compare Vocabulary Differences Between Ranking Web Pages On SERP With PythonScreenshot from creator, August 2022

You may see our consequence URLs and all their on-page SEO parts, together with response headers, response sizes, and structured knowledge info.

5. Tokenize The Content material Of The Internet Pages For Textual content Processing In NLP Methodologies

Tokenization of the content material of the online pages requires selecting the “body_text” column of advertools crawl output and utilizing the “word_tokenize” from NLTK.

crawled_df["body_text"][0]

The code line above calls your complete content material of one of many consequence pages as beneath.

Compare Vocabulary Differences Between Ranking Web Pages On SERP With PythonScreenshot from creator, August 2022

To tokenize these sentences, use the code block beneath.

tokenized_words = word_tokenize(crawled_df["body_text"][0])

len(tokenized_words)

We tokenized the content material of the primary doc and checked what number of phrases it had.

Compare Vocabulary Differences Between Ranking Web Pages On SERP With PythonScreenshot from creator, August 2022

The primary doc we tokenized for the question “search engine marketing” has 11211 phrases. And boilerplate content material is included on this quantity.

6. Take away The Punctuations And Cease Phrases From Corpus

Take away the punctuations, and the cease phrases, as beneath.

stop_words = set(stopwords.phrases("english"))
tokenized_words = [word for word in tokenized_words if not word.lower() in stop_words and word.lower() not in string.punctuation]

len(tokenized_words)

Clarification of code block above:

 

    • Create a set with the “stopwords.phrases(“english”)” to incorporate all of the cease phrases within the English language. Python units don’t embody duplicate values; thus, we used a set reasonably than an inventory to forestall any battle.

 

    • Use record comprehension with “if” and “else” statements.

 

    • Use the “decrease” technique to check the “And” or “To” varieties of phrases correctly to their lowercase variations within the cease phrases record.

 

    • Use the “string” module and embody “punctuations.” A be aware right here is that the string module may not embody all of the punctuations that you just would possibly want. For these conditions, create your individual punctuation record and change these characters with area utilizing the regex, and “regex.sub.”

 

    • Optionally, to take away the punctuations or another non-alphabetic and numeric values, you need to use the “isalnum” technique of Python strings. However, primarily based on the phrases, it would give completely different outcomes. For instance, “isalnum” would take away a phrase comparable to “keyword-related” for the reason that “-” on the center of the phrase is just not alphanumeric. However, string.punctuation wouldn’t take away it since “keyword-related” is just not punctuation, even when the “-” is.

 

    • Measure the size of the brand new record.

 

The brand new size of our tokenized glossary is “5319”. It reveals that almost half of the vocabulary of the doc consists of cease phrases or punctuations.

 

It’d imply that solely 54% of the phrases are contextual, and the remainder is purposeful.

 

7. Rely The Quantity Of Occurrences Of The Phrases In The Content material Of The Internet Pages

 

To depend the occurrences of the phrases from the corpus, the “Counter” object from the “Collections” module is used as beneath.

 

counted_tokenized_words = Counter(tokenized_words)

counts_of_words_df = pd.DataFrame.from_dict(

    counted_tokenized_words, orient="index").reset_index()

counts_of_words_df.sort_values(by=0, ascending=False, inplace=True)

counts_of_words_df.head(50)

A proof of the code block is beneath.

  • Create a variable comparable to “counted_tokenized_words” to contain the Counter technique outcomes.
  • Use the “DataFrame” constructor from the Pandas to assemble a brand new knowledge body from Counter technique outcomes for the tokenized and cleaned textual content.
  • Use the “from_dict” technique as a result of “Counter” offers a dictionary object.
  • Use “sort_values” with “by=0” which suggests kind primarily based on the rows, and “ascending=False” means to place the very best worth to the highest. “Inpace=True” is for making the brand new sorted model everlasting.
  • Name the primary 50 rows with the “head()” technique of pandas to check the first look of the data body.

You may see the consequence beneath.

counts of wordsScreenshot from creator, August 2022

We don’t see a cease phrase on the outcomes, however some fascinating punctuation marks stay.

That occurs as a result of some web sites use completely different characters for a similar functions, comparable to curly quotes (good quotes), straight single quotes, and double straight quotes.

And string module’s “capabilities” module doesn’t contain these.

Thus, to scrub our knowledge body, we’ll use a custom lambda operate as beneath.

removed_curly_quotes = "’“”"

counts_of_words_df["index"] = counts_of_words_df["index"].apply(lambda x: float("NaN") if x in removed_curly_quotes else x)

counts_of_words_df.dropna(inplace=True)

counts_of_words_df.head(50)

Clarification of code block:

  • Created a variable named “removed_curly_quotes” to contain a curly single, double quotes, and straight double quotes.
  • Used the “apply” operate in pandas to verify all columns with these potential values.
  • Used the lambda operate with “float(“NaN”) in order that we are able to use “dropna” technique of Pandas.
  • Use “dropna” to drop any NaN worth that replaces the precise curly quote variations. Add “inplace=True” to drop NaN values completely.
  • Name the dataframe’s new model and verify it.

You may see the consequence beneath.

counts of words dfScreenshot from creator, August 2022

We see essentially the most used phrases within the “Search Engine Optimization” associated rating internet doc.

With Panda’s “plot” methodology, we are able to visualize it simply as beneath.

counts_of_words_df.head(20).plot(form="bar",x="index", orientation="vertical", figsize=(15,10), xlabel="Tokens", ylabel="Rely", colormap="viridis", desk=False, grid=True, fontsize=15, rot=35, place=1, title="Token Counts from a Web site Content material with Punctiation", legend=True).legend(["Tokens"], loc="decrease left", prop={"measurement":15})

Clarification of code block above:

  • Use the top technique to see the primary significant values to have a clear visualization.
  • Use “plot” with the “form” attribute to have a “bar plot.”
  • Put the “x” axis with the columns that contain the phrases.
  • Use the orientation attribute to specify the course of the plot.
  • Decide figsize with a tuple that specifies top and width.
  • Put x and y labels for x and y axis names.
  • Decide a colormap that has a assemble comparable to “viridis.”
  • Decide font measurement, label rotation, label place, the title of plot, legend existence, legend title, location of legend, and measurement of the legend.

The Pandas DataFrame Plotting is an in depth subject. If you wish to use the “Plotly” as Pandas visualization back-end, verify the Visualization of Sizzling Matters for Information SEO.

You may see the consequence beneath.

Pandas DataFrame PlottingPicture from creator, August 2022

Now, we are able to select our second URL to begin our comparability of vocabulary measurement and prevalence of phrases.

8. Select The Second URL For Comparability Of The Vocabulary Dimension And Occurrences Of Phrases

To check the earlier search engine marketing content material to a competing internet doc, we’ll use SEJ’s search engine marketing information. You may see a compressed model of the steps adopted till now for the second article.

def tokenize_visualize(article:int):

    stop_words = set(stopwords.phrases("english"))

    removed_curly_quotes = "’“”"

    tokenized_words = word_tokenize(crawled_df["body_text"][article])

    print("Rely of tokenized phrases:", len(tokenized_words))

    tokenized_words = [word for word in tokenized_words if not word.lower() in stop_words and word.lower() not in string.punctuation and word.lower() not in removed_curly_quotes]

    print("Rely of tokenized phrases after removing punctations, and cease phrases:", len(tokenized_words))

    counted_tokenized_words = Counter(tokenized_words)

    counts_of_words_df = pd.DataFrame.from_dict(

    counted_tokenized_words, orient="index").reset_index()

    counts_of_words_df.sort_values(by=0, ascending=False, inplace=True)

    #counts_of_words_df["index"] = counts_of_words_df["index"].apply(lambda x: float("NaN") if x in removed_curly_quotes else x)

    counts_of_words_df.dropna(inplace=True)

    counts_of_words_df.head(20).plot(form="bar",

    x="index",

    orientation="vertical",

    figsize=(15,10),

    xlabel="Tokens",

    ylabel="Rely",

    colormap="viridis",

    desk=False,

    grid=True,

    fontsize=15,

    rot=35,

    place=1,

    title="Token Counts from a Web site Content material with Punctiation",

    legend=True).legend(["Tokens"],

    loc="decrease left",

    prop={"measurement":15})

We collected every little thing for tokenization, removing of cease phrases, punctations, changing curly quotations, counting phrases, knowledge body development, knowledge body sorting, and visualization.

Under, you may see the consequence.

tokenize visualization resultsScreenshot by creator, August 2022

The SEJ article is within the eighth rating.

tokenize_visualize(8)

The quantity eight means it ranks eighth on the crawl output knowledge body, equal to the SEJ article for search engine marketing. You may see the consequence beneath.

token counts from website content and punctuationPicture from creator, August 2022

We see that the 20 most used phrases between the SEJ search engine marketing article and different competing search engine marketing articles differ.

9. Create A Customized Operate To Automate Phrase Prevalence Counts And Vocabulary Distinction Visualization

The basic step to automating any search engine marketing activity with Python is wrapping all of the steps and requirements below a sure Python operate with completely different potentialities.

The operate that you will notice beneath has a conditional assertion. In case you cross a single article, it makes use of a single visualization name; for a number of ones, it creates sub-plots in keeping with the sub-plot depend.

def tokenize_visualize(articles:record, article:int=None):

     if article:

          stop_words = set(stopwords.phrases("english"))

          removed_curly_quotes = "’“”"

          tokenized_words = word_tokenize(crawled_df["body_text"][article])

          print("Rely of tokenized phrases:", len(tokenized_words))

          tokenized_words = [word for word in tokenized_words if not word.lower() in stop_words and word.lower() not in string.punctuation and word.lower() not in removed_curly_quotes]

          print("Rely of tokenized phrases after removing punctations, and cease phrases:", len(tokenized_words))

          counted_tokenized_words = Counter(tokenized_words)

          counts_of_words_df = pd.DataFrame.from_dict(

          counted_tokenized_words, orient="index").reset_index()

          counts_of_words_df.sort_values(by=0, ascending=False, inplace=True)

          #counts_of_words_df["index"] = counts_of_words_df["index"].apply(lambda x: float("NaN") if x in removed_curly_quotes else x)

          counts_of_words_df.dropna(inplace=True)

          counts_of_words_df.head(20).plot(form="bar",

          x="index",

          orientation="vertical",

          figsize=(15,10),

          xlabel="Tokens",

          ylabel="Rely",

          colormap="viridis",

          desk=False,

          grid=True,

          fontsize=15,

          rot=35,

          place=1,

          title="Token Counts from a Web site Content material with Punctiation",

          legend=True).legend(["Tokens"],

          loc="decrease left",

          prop={"measurement":15})

     

     if articles:

          source_names = []

          for i in vary(len(articles)):

               source_name = crawled_df["url"][articles[i]]

               print(source_name)

               source_name = urlparse(source_name)

               print(source_name)

               source_name = source_name.netloc

               print(source_name)

               source_names.append(source_name)

          world dfs

          dfs = []

          for i in articles:

               stop_words = set(stopwords.phrases("english"))

               removed_curly_quotes = "’“”"

               tokenized_words = word_tokenize(crawled_df["body_text"][i])

               print("Rely of tokenized phrases:", len(tokenized_words))

               tokenized_words = [word for word in tokenized_words if not word.lower() in stop_words and word.lower() not in string.punctuation and word.lower() not in removed_curly_quotes]

               print("Rely of tokenized phrases after removing punctations, and cease phrases:", len(tokenized_words))

               counted_tokenized_words = Counter(tokenized_words)

               counts_of_words_df = pd.DataFrame.from_dict(

               counted_tokenized_words, orient="index").reset_index()

               counts_of_words_df.sort_values(by=0, ascending=False, inplace=True)

               #counts_of_words_df["index"] = counts_of_words_df["index"].apply(lambda x: float("NaN") if x in removed_curly_quotes else x)

               counts_of_words_df.dropna(inplace=True)

               df_individual = counts_of_words_df

               dfs.append(df_individual)

               

          import matplotlib.pyplot as plt

          determine, axes = plt.subplots(len(articles), 1)

          for i in vary(len(dfs) + 0):

               dfs[i].head(20).plot(ax = axes[i], form="bar",

                    x="index",

                    orientation="vertical",

                    figsize=(len(articles) * 10, len(articles) * 10),

                    xlabel="Tokens",

                    ylabel="Rely",

                    colormap="viridis",

                    desk=False,

                    grid=True,

                    fontsize=15,

                    rot=35,

                    place=1,

                    title= f"{source_names[i]} Token Counts",

                    legend=True).legend(["Tokens"],

                    loc="decrease left",

                    prop={"measurement":15})

To maintain the article concise, I gained’t add an evidence for these. Nonetheless, in the event you verify earlier SEJ Python search engine marketing tutorials I’ve written, you’ll notice comparable wrapper capabilities.

Let’s use it.

tokenize_visualize(articles=[1, 8, 4])

We needed to take the primary, eighth, and fourth articles and visualize their prime 20 phrases and their occurrences; you may see the consequence beneath.

visualization of top 20 wordsPicture from creator, August 2022

10. Examine The Distinctive Phrase Rely Between The Paperwork

Evaluating the distinctive phrase depend between the paperwork is sort of straightforward, because of pandas. You may verify the customized operate beneath.

def compare_unique_word_count(articles:record):

     source_names = []

     for i in vary(len(articles)):

          source_name = crawled_df["url"][articles[i]]

          source_name = urlparse(source_name)

          source_name = source_name.netloc

          source_names.append(source_name)


     stop_words = set(stopwords.phrases("english"))

     removed_curly_quotes = "’“”"

     i = 0

     for article in articles:

          textual content = crawled_df["body_text"][article]

          tokenized_text = word_tokenize(textual content)

          tokenized_cleaned_text = [word for word in tokenized_text if not word.lower() in stop_words if not word.lower() in string.punctuation if not word.lower() in removed_curly_quotes]

          tokenized_cleanet_text_counts = Counter(tokenized_cleaned_text)

          tokenized_cleanet_text_counts_df = pd.DataFrame.from_dict(tokenized_cleanet_text_counts, orient="index").reset_index().rename(columns={"index": source_names[i], 0: "Counts"}).sort_values(by="Counts", ascending=False)

          i += 1

          print(tokenized_cleanet_text_counts_df, "Variety of distinctive phrases: ",  tokenized_cleanet_text_counts_df.nunique(), "Complete contextual phrase depend: ", tokenized_cleanet_text_counts_df["Counts"].sum(), "Complete phrase depend: ", len(tokenized_text))

compare_unique_word_count(articles=[1, 8, 4])

The result’s beneath.

The underside of the consequence reveals the variety of distinctive values, which reveals the variety of distinctive phrases within the doc.

 www.wordstream.com  Counts

16               Google      71

82                  search engine marketing      66

186              search      43

228                web site      28

274                web page      27

…                 …     …

510   markup/structured       1

1                Current       1

514             mistake       1

515              backside       1

1024           LinkedIn       1

[1025 rows x 2 columns] Variety of distinctive phrases:

 www.wordstream.com    1025

Counts                  24

dtype: int64 Complete contextual phrase depend:  2399 Complete phrase depend:  4918

    www.searchenginejournal.com  Counts

9                           search engine marketing      93

242                      search      25

64                        Information      23

40                      Content material      17

13                       Google      17

..                          …     …

229                      Motion       1

228                      Shifting       1

227                       Agile       1

226                          32       1

465                        information       1

[466 rows x 2 columns] Variety of distinctive phrases:

 www.searchenginejournal.com    466

Counts                          16

dtype: int64 Complete contextual phrase depend:  1019 Complete phrase depend:  1601

     weblog.hubspot.com  Counts

166               search engine marketing      86

160            search      76

32            content material      46

368              web page      40

327             hyperlinks      39

…               …     …

695              thought       1

697            talked       1

698           earlier       1

699         Analyzing       1

1326         Security       1

[1327 rows x 2 columns] Variety of distinctive phrases:

 weblog.hubspot.com    1327

Counts                31

dtype: int64 Complete contextual phrase depend:  3418 Complete phrase depend:  6728

There are 1025 distinctive phrases out of 2399 non-stopword and non-punctuation contextual phrases. The entire phrase depend is 4918.

Probably the most used 5 phrases are “Google,” “search engine marketing,” “search,” “web site,” and “web page” for “Wordstream.” You may see the others with the identical numbers.

11. Examine The Vocabulary Variations Between The Paperwork On The SERP

Auditing what distinctive phrases seem in competing paperwork helps you see the place the doc weighs extra and the way it creates a distinction.

The methodology is easy: “set” object kind has a “distinction” technique to point out the completely different values between two units.

def audit_vocabulary_difference(articles:record):

     stop_words = set(stopwords.phrases("english"))

     removed_curly_quotes = "’“”"

     world dfs

     world source_names

     source_names = []

     for i in vary(len(articles)):

          source_name = crawled_df["url"][articles[i]]

          source_name = urlparse(source_name)

          source_name = source_name.netloc

          source_names.append(source_name)

     i = 0

     dfs = []

     for article in articles:

               textual content = crawled_df["body_text"][article]

               tokenized_text = word_tokenize(textual content)

               tokenized_cleaned_text = [word for word in tokenized_text if not word.lower() in stop_words if not word.lower() in string.punctuation if not word.lower() in removed_curly_quotes]

               tokenized_cleanet_text_counts = Counter(tokenized_cleaned_text)

               tokenized_cleanet_text_counts_df = pd.DataFrame.from_dict(tokenized_cleanet_text_counts, orient="index").reset_index().rename(columns={"index": source_names[i], 0: "Counts"}).sort_values(by="Counts", ascending=False)

               tokenized_cleanet_text_counts_df.dropna(inplace=True)

               i += 1

               df_individual = tokenized_cleanet_text_counts_df

               dfs.append(df_individual)

     world vocabulary_difference

     vocabulary_difference = []

     for i in dfs:

          vocabulary = set(i.iloc[:, 0].to_list())

          vocabulary_difference.append(vocabulary)

     print( "Phrases that seem on :", source_names[0], "however not on: ", source_names[1], "are beneath: n", vocabulary_difference[0].distinction(vocabulary_difference[1]))

To maintain issues concise, I gained’t clarify the operate traces one after the other, however principally, we take the distinctive phrases in a number of articles and examine them to one another.

You may see the consequence beneath.

Phrases that seem on: www.techtarget.com however not on: moz.com are beneath:

Compare Vocabulary Differences Between Ranking Web Pages On SERP With PythonScreenshot by creator, August 2022

Use the customized operate beneath to see how usually these phrases are used within the particular doc.

def unique_vocabulry_weight():

     audit_vocabulary_difference(articles=[3, 1])

vocabulary_difference_list = vocabulary_difference_df[0].to_list()

     return dfs[0][dfs[0].iloc[:, 0].isin(vocabulary_difference_list)]

unique_vocabulry_weight()

The outcomes are beneath.

Compare Vocabulary Differences Between Ranking Web Pages On SERP With PythonScreenshot by creator, August 2022

The vocabulary distinction between TechTarget and Moz for the “search engine marketing” question from TechTarget’s perspective is above. We are able to reverse it.

def unique_vocabulry_weight():

audit_vocabulary_difference(articles=[1, 3])

vocabulary_difference_list = vocabulary_difference_df[0].to_list()

return dfs[0][dfs[0].iloc[:, 0].isin(vocabulary_difference_list)]

unique_vocabulry_weight()

Change the order of numbers. Examine from one other perspective.

moz counts resultsScreenshot by creator, August 2022

You may see that Wordstream has 868 distinctive phrases that don’t seem on Boosmart, and the highest 5 and tail 5 are given above with their occurrences.

The vocabulary distinction audit could be improved with “weighted frequency” by checking the question info and community.

However, for instructing functions, that is already a heavy, detailed, and superior Python, Knowledge Science, and search engine marketing intensive course.

See you within the subsequent guides and tutorials.

Extra sources:


Featured Picture: VectorMine/Shutterstock

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