AI Neural Topic Modeling for SEO Clustering

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The AI-Powered Neural Topic Modeling for Content Clustering and SEO Strategy project aims to use advanced AI technology (specifically Neural Topic Modeling) to help website owners understand their content better. The project aims to:

Automatically organize content into meaningful groups (called clusters) based on the discussed topics.
Improve the website’s SEO (Search Engine Optimization) by identifying the best keywords and linking similar pages together to boost visibility in search engine rankings.
Recommend similar content to users, helping them easily find other relevant pages on the website.

Let’s break down each part in simple language:

1. Neural Topic Modeling (NTM):

Neural Topic Modeling (NTM) is a powerful AI-driven technique designed to analyse large volumes of textual data and automatically uncover the underlying themes present within that content. These themes, known as topics, represent the recurring ideas, subjects, or areas of focus that appear throughout a website’s pages, blogs, or articles.

Unlike traditional keyword analysis, which relies on predefined terms, NTM works by understanding patterns in language. It examines how words and phrases naturally appear together across different pieces of content. By doing so, it can identify topics without human intervention. In simple terms, it allows artificial intelligence to “read” your website and determine what it is truly about.

For example, if your website contains multiple articles covering SEO practices, digital marketing trends, analytics, and web development, Neural Topic Modeling will recognise these as distinct topics. Even if the articles use varied wording or phrasing, the AI can still group them correctly by understanding context rather than relying on exact matches.

This capability is extremely valuable for websites with growing content libraries. As more pages are added over time, NTM continuously adapts, ensuring that emerging themes are identified and existing ones are refined. The result is a deeper, more accurate understanding of your content landscape.

2. Content Clustering:

Once Neural Topic Modeling identifies the key topics, the next step is content clustering. Content clustering involves grouping together pages or articles that share the same or closely related themes. Each cluster represents a specific topic area on your website.

Think of content clustering as organising a large library. Instead of having books scattered randomly, they are neatly placed into sections such as marketing, technology, or business strategy. Similarly, if your website has multiple articles discussing SEO strategies, keyword research, and technical optimisation, the system automatically places them into a single SEO-focused cluster.

This structured grouping offers several advantages. From a user perspective, it makes navigation smoother and more intuitive. Visitors can easily explore related content without needing to search manually. From a management perspective, it gives website owners a clear overview of what content exists, what topics are well-covered, and where gaps may be present.

Content clustering also ensures consistency across topic areas. It helps maintain thematic relevance, which is crucial for both user satisfaction and search engine interpretation.

3. SEO Strategy: How Does It Help SEO?

Search Engine Optimisation (SEO) focuses on improving a website’s visibility on search engines such as Google. Higher rankings mean increased traffic, better brand exposure, and more opportunities for conversions. This project enhances SEO by using Neural Topic Modeling in two key ways.

Keyword Strategy:

NTM identifies the most relevant and high-impact keywords associated with each topic cluster. Instead of guessing which keywords to target, website owners gain data-backed insights into the terms users are most likely searching for. For instance, if the system detects strong relevance around topics like “SEO services” and “link-building,” those terms can be strategically prioritised within the content.

Internal Linking:

The project also analyses content similarity across pages. By understanding which pages are closely related, it provides guidance on internal linking opportunities. Internal links help search engines understand the structure of your website and the relationships between different pages. This improves crawlability, indexing efficiency, and overall ranking potential.

Together, these strategies ensure that your website aligns more closely with how search engines interpret content relevance and authority.

4. Recommendation System: What Does It Do?

Beyond organisation and optimisation, the project functions as an intelligent recommendation system. When a visitor views a specific page, the system analyses its topic and suggests other pages with similar or complementary content.

For example, if a user is reading an article about SEO strategies, the recommendation system might suggest related pages on competitor keyword analysis, technical audits, or link-building techniques. These recommendations are context-aware, meaning they are based on actual content similarity rather than generic rules.

This approach significantly enhances user engagement. Visitors are more likely to explore additional pages, spend more time on the site, and interact with multiple pieces of content. As a result, bounce rates decrease while session duration increases—both of which are positive signals for search engines.

5. How Does This Help a Website Owner?

From a website owner’s perspective, this project delivers measurable value across multiple areas.

Content Clustering:

Manual organisation of content becomes unnecessary. The system automatically groups pages, saving time and reducing the risk of misclassification.

SEO Optimisation:

With clear keyword insights and internal linking suggestions, website owners can implement targeted improvements that directly impact search visibility.

User Engagement:

By offering relevant content recommendations, the website becomes more engaging and user-friendly, leading to higher retention and improved conversion potential.

Overall, the project transforms content management from a manual, time-consuming process into an intelligent, automated system.

Example of How It Works:

Imagine you own a website offering digital marketing services. Your pages include:

SEO services
Social media marketing
Link-building techniques
Content proofreading

Using this project:

Neural Topic Modeling analyses all pages and identifies core themes such as SEO, social media, and content services.
The system clusters related pages into organised topic groups.
It suggests high-value keywords for each topic, helping you optimise content effectively.
It highlights which pages should be internally linked, strengthening site structure.
It generates personalised content recommendations for users based on the pages they view.

Key Benefits for Website Owners:

Save time by automating content organisation
Improve SEO through data-driven keyword insights and internal linking
Increase user engagement with intelligent content recommendations

This integrated approach ensures that both users and search engines experience your website at its best.

What is Neural Topic Modeling (NTM)?

Neural Topic Modeling combines traditional topic modeling techniques (like Latent Dirichlet Allocation, LDA) with neural networks. Topic modeling is a process that discovers hidden topics or themes within a large collection of text data. Neural Topic Modeling enhances this by using deep learning (neural networks) to identify complex, nuanced topics in the content, improving the accuracy of topic discovery.

Use Cases of Neural Topic Modeling:

Content Organization:
Neural Topic Modeling (NTM) enables websites to automatically structure large volumes of content into clearly defined thematic groups. Instead of relying on manual tagging or rigid taxonomies, NTM analyses contextual relationships between words and sentences to uncover natural topic boundaries. This makes it significantly easier to build content clusters, pillar pages, and internal linking frameworks. As a result, websites achieve better navigation, improved crawl efficiency for search engines, and a more intuitive reading experience for users seeking related information.
SEO Optimization:
NTM plays a crucial role in modern SEO by identifying latent themes hidden deep within existing content. Rather than focusing only on surface-level keywords, it reveals semantic patterns and topic gaps that traditional keyword tools often miss. These insights help guide content expansion, refine keyword targeting, and align pages with user search intent. By mapping content to concept-driven topics, websites can strengthen topical authority, reduce keyword cannibalisation, and improve rankings across a broader set of relevant queries.
Recommendation Systems:
Content platforms and e-commerce websites leverage NTM to power intelligent recommendation engines. By understanding topic-level similarities between articles, products, or user interactions, NTM enables highly relevant suggestions. This leads to personalised browsing experiences, higher engagement, and improved conversion rates. Recommendations become context-aware rather than rule-based, adapting dynamically as new content or products are introduced.

Real-Life Implementations:

Customer Reviews Analysis:
E-commerce platforms use NTM to process thousands of customer reviews at scale and identify the themes customers care about most. Topics such as delivery speed, product durability, pricing concerns, or customer service emerge organically from unstructured feedback. Businesses can then prioritise improvements, refine messaging, and respond strategically to customer sentiment without manual review analysis.
News Websites:
News publishers rely on NTM to automatically categorise and group related stories across evolving topics. This allows real-time creation of content hubs around ongoing events, trends, or issues. It also improves content discovery, encourages deeper reader engagement, and supports long-term archival organisation.
Search Engines:
Search engines apply NTM to enhance query understanding and content classification. By recognising nuanced topic relationships, they deliver more precise and context-aware search results. This improves relevance, reduces ambiguity, and supports advanced features such as semantic search and conversational queries.

How is NTM used on Websites?

For website-based projects, Neural Topic Modeling (NTM) is applied to examine large volumes of textual content and identify meaningful thematic patterns across pages. It works by analysing blogs, landing pages, product descriptions, service pages, and other written assets to automatically group them into logical topic areas. This approach is especially valuable for modern, content-heavy websites where manual categorisation becomes inefficient.

This process is highly effective for:

Optimising SEO and Keywords:

NTM goes beyond surface-level keyword matching. It uncovers hidden semantic topics embedded within your content, even when exact keywords are not repeated. These insights help refine keyword targeting, improve topical authority, and align pages with how search engines now interpret intent-driven queries. As a result, websites can achieve stronger rankings and better visibility across relevant search terms.

Content Clustering:

By identifying related themes, NTM enables the creation of structured content clusters. These clusters improve internal linking, make content easier to explore, and help users navigate related topics naturally. Well-organised clusters also signal topical relevance to search engines, strengthening overall SEO performance.

What kind of data does NTM need?

Text Data:

NTM relies on substantial amounts of text to function effectively. For website projects, this includes all written materials such as blog posts, service descriptions, category pages, FAQs, and informational content. The richer and more diverse the textual dataset, the more accurate and insightful the topic modelling results become.

Input Formats:

Text data can be supplied in multiple formats. One common method is providing URLs, where the system scrapes and extracts text directly from web pages. Another option is using structured datasets such as CSV files. In this case, the content is organised into columns—typically including page titles, URLs, and body text. Proper structuring ensures smoother processing and more precise topic discovery.

How does NTM work technically?

Preprocessing the Data:

Before analysis begins, the text undergoes preprocessing. This includes cleaning tasks such as removing stopwords (for example, “the,” “and,” “is”), eliminating noise, and standardising word forms. After cleaning, the text is transformed into numerical representations through vectorisation, allowing neural networks to interpret and process language mathematically.

Neural Network and Topic Discovery:

Once vectorised, the data is fed into a neural network designed to detect deeper semantic relationships. Unlike traditional models such as Latent Dirichlet Allocation (LDA), which rely on simpler probability-based assumptions, NTM captures complex contextual patterns. This enables it to recognise nuanced themes, overlapping topics, and evolving content relationships across the website.

Output:

After processing, NTM produces a structured output consisting of topic groups represented by key terms and associated pages. For a website, this reveals dominant themes, subtopics, and content gaps. These insights provide clear direction for content optimisation, internal linking, and future publishing strategies.

Why is NTM helpful for content clustering and keyword strategies?

By uncovering hidden semantic topics, NTM enables:

Optimised Content Clusters:

Related pages are grouped intelligently, enhancing site structure and improving user engagement through intuitive navigation.

Enhanced Keyword Strategy:

The discovered topics highlight relevant keywords and search intents, helping refine SEO efforts and align content with how users actually search online.

1. Import Required Libraries for the Project

Purpose: requests is a Python library used to make HTTP requests. In this project, we use it to access the content of the web pages listed in the URLs. When we “request” a webpage, this library gets the HTML content of that webpage for us to work with.

Purpose: BeautifulSoup is a library used for parsing HTML and XML documents. Webpages are written in HTML, and this tool helps extract only the relevant text (ignoring HTML tags like <div>, <p>, etc.). It’s like scraping the meaningful content from the raw HTML that the requests library fetches.

Purpose: re is Python’s regular expression module. Regular expressions are used for searching, matching, and manipulating text. We use it here to clean the text (e.g., removing digits, punctuation, or unwanted characters) before we analyze it.

Purpose: nltk is the Natural Language Toolkit, a powerful library for processing text. It provides tools for tasks such as tokenization (splitting text into words), removing stopwords (like “the,” “is,” etc.), and performing other natural language processing (NLP) tasks.

Purpose: The stopwords corpus is a part of the nltk library and contains lists of common words in a language (like “the,” “is,” “in,” etc.). These words are not very informative for analysis, so we remove them from the text to focus only on meaningful words.

Purpose: CountVectorizer is a tool that transforms text into a Bag of Words model. This converts the text into a matrix of word counts where each row represents a document, and each column represents a word. This is essential because machine learning models like Neural Topic Modeling need text in a numerical format to work with.

Purpose: cosine_similarity is used to calculate how similar two vectors are. In this project, it calculates the similarity between the topics of different web pages. Two pages that have a high cosine similarity score are considered to be about similar subjects, which is useful for making content recommendations.

Purpose: corpora and models are part of the Gensim library, which is used for topic modeling. In this case, we use them to build the Latent Dirichlet Allocation (LDA) model. The LDA model automatically discovers topics from text data by analyzing word patterns across documents (in this case, the web pages).
corpora: Helps create a dictionary that maps words to their unique IDs.
models: Contains tools to build the LDA model, which will discover the hidden topics in the content.

Purpose: numpy is a library for working with arrays and matrices, which are essential data structures in machine learning. Here, it is used to store the topic distributions (i.e., how much each topic is present in each webpage) in matrix form. It’s also useful for mathematical operations.

2. Download English Stopwords Using NLTK

Purpose: Before using the list of stopwords (common words that don’t add value to the analysis), we need to download them using nltk. This command ensures that we have the necessary stopwords in English to remove from the text before further processing.

Function: fetch_webpage_content

This function takes a list of URLs as input and returns a list of raw text extracted from each webpage.

1. Define the function and initialize the list:

Explanation:
- The function fetch_webpage_content accepts one parameter called urls, which is a list of web page URLs.
- content_list is an empty list that will be used to store the text content extracted from each webpage.
Example: In this case, the input could be:

2. Loop over each URL:

· Explanation:

This loop goes through each URL in the list urls. It will repeat the process for every URL to retrieve the content.

· Example: The URL https://thatware.co/ will be the first (and only) URL in this case, so the function will loop through it.

3. Send an HTTP request to get the webpage content:

· Explanation:

requests.get(url) sends an HTTP request to the website server and tries to download the webpage’s content.
The response from the server is stored in the variable response. This contains the HTML content of the webpage.

· Example:

For the URL https://thatware.co/, this sends a request to the server hosting the ThatWare website. The response will contain the HTML code for the homepage.

4. Parse the HTML content:

· Explanation:

The BeautifulSoup object soup parses the HTML content (which is in response.content) and allows us to work with it as a structured document.
html.parser is a built-in HTML parser that helps break down the HTML into meaningful elements.

· Example:

The BeautifulSoup library will now read through the HTML from https://thatware.co/ and allow us to extract text from the page, ignoring the HTML tags (like <div>, <h1>, etc.).

5. Extract visible text from the webpage:

· Explanation:

soup.get_text(separator=’ ‘) extracts all the visible text from the webpage (without HTML tags). The separator=’ ‘ argument ensures that different blocks of text are separated by spaces.
The result is a long string containing all the visible text on the page, with spaces between different sections.

· Example:

From the page https://thatware.co/, this will extract the visible text content such as the homepage’s headers, body text, and any other readable content, ignoring HTML code like <div>, <h1>, etc.

6. Add the extracted text to the list:

· Explanation:

The extracted text is then added to the content_list. This ensures that for each URL processed in the loop, we store its content in this list.

· Example:

For the URL https://thatware.co/, the visible text of the homepage (such as “ThatWare SEO Services,” “AI-Powered SEO Solutions,” etc.) will be added as a string to content_list.

7. Handle any errors that occur:

Explanation:
- The try block ensures that if something goes wrong (e.g., the webpage cannot be reached, or the URL is incorrect), the except block will handle it.
- It prints an error message that includes the URL that caused the error and a description of the error (str(e)).
Example:
- If there’s an issue fetching the content from https://thatware.co/, this part of the code will catch the error and print something like:

8. Return the list of content:

Explanation:
- After all the URLs have been processed, the function returns content_list, which contains the extracted text from each webpage.
Example:
- For https://thatware.co/, the function would return a list containing the visible text from the homepage:

Complete Example:

Let’s say we call this function with one URL:

The output would be a list containing the text content of the page, similar to:

What Does This Function Do?

The preprocess_text function is designed to clean text data. When we collect raw text from webpages, it contains unnecessary information like punctuation, numbers, and common words like “the,” “and,” “is.”

Step-by-Step Explanation

1. Initialize the Stopwords:

· Explanation:

The stopwords.words(‘english’) gets a list of common English words that are not useful for analysis (like “the,” “is,” “and”).
These words are called stopwords, and they don’t help when you are trying to understand the main ideas or topics in a document.
set(stopwords.words(‘english’)) stores these stopwords in a set (which is a type of collection that makes checking for words faster).

· Why this is important:

Removing these common words helps us focus on the more important and meaningful words in the text.

2. Add Custom Stopwords and Symbols:

· Explanation:

In addition to the default stopwords from the nltk library, this line defines custom stopwords and symbols that we also want to remove.
This includes:
- Extra common words: “the,” “and,” “when,” “where,” etc.
- Punctuation: Symbols like “:”, “;”, “(“, “)”, etc.
These words and symbols are not useful for understanding the topics in the text, so we will remove them.

· Why this is important:

By removing symbols and unnecessary words, we make the text cleaner and more meaningful for further analysis.

3. Merge Default and Custom Stopwords:

· Explanation:

This line combines the default stopwords from nltk with the custom stopwords and symbols that were defined above.
The union() function merges these two lists together, so we have one complete list of all the words and symbols we want to remove.

· Why this is important:

It ensures that all the unnecessary words (default and custom) will be removed when we clean the text.

4. Create an Empty List to Store the Cleaned Text:

· Explanation:

This line creates an empty list called preprocessed_content.
As we process each webpage’s text and clean it, the cleaned version of the text will be added to this list.

· Why this is important:

This list will store the final, cleaned versions of the text for all the webpages, so we can use it later in the analysis.

5. Start Looping Through Each Webpage’s Content:

· Explanation:

This line starts a loop that goes through each piece of text in content_list.
content_list is the list of raw text from each webpage (from the previous step where we fetched the content from the URLs).

· Why this is important:

This loop allows us to process each webpage’s content one by one.

6. Convert All Text to Lowercase:

· Explanation:

This converts the entire text of each webpage to lowercase.
For example, “SEO Services” becomes “seo services.”

· Why this is important:

By converting everything to lowercase, we treat words like “SEO” and “seo” as the same. This makes the text uniform and avoids confusion when analyzing it.

7. Remove Digits (Numbers):

· Explanation:

This line uses a regular expression (re.sub) to remove digits from the text.
The pattern r’\d+’ matches any sequence of numbers, and ” replaces those numbers with nothing (effectively deleting them).

· Why this is important:

Numbers are usually not useful when analyzing topics in text, so removing them helps make the text cleaner and easier to analyze.

8. Remove Punctuation and Symbols:

· Explanation:

This line removes punctuation and special symbols from the text using another regular expression.
The pattern r'[^\w\s]’ matches any character that is not a word character (\w) or a whitespace (\s), meaning it removes everything else (punctuation, symbols, etc.).

· Why this is important:

Punctuation and symbols don’t add meaning to the text, so removing them makes the text cleaner for further processing.

9. Split the Text into Words:

· Explanation:

This line splits the text into a list of words.
For example, the sentence “seo services digital marketing” will be split into [‘seo’, ‘services’, ‘digital’, ‘marketing’].

· Why this is important:

This makes it easier to remove stopwords (we can now look at each word individually) and analyze the text later.

10. Remove Stopwords:

· Explanation:

This line goes through each word in the words list and checks if it is in the stop_words set (which includes default and custom stopwords).
If the word is not in stop_words, it is kept. Otherwise, it is removed.
The cleaned list of words (without stopwords) is stored in filtered_words.

· Why this is important:

Removing stopwords helps focus the analysis on the meaningful words, rather than common, less important ones like “the,” “is,” “and.”

11. Rejoin the Cleaned Words and Add to Final List:

· Explanation:

This takes the list of cleaned words (filtered_words) and joins them back into a single string (with spaces between the words).
The cleaned text is then added to the preprocessed_content list.

· Why this is important:

This step completes the cleaning process for each piece of text and stores the cleaned version so that it can be used later in the analysis.

12. Return the Preprocessed Text:

· Explanation:

After processing all the webpages, the function returns the list of cleaned text (preprocessed_content).
This cleaned text can now be used for further analysis, such as building a topic model or finding related content.

· Why this is important:

We need the cleaned text to move on to the next steps in the analysis. This is the final output of this step.

Example of How the Function Works:

Let’s say we have some raw text:

After running it through the preprocess_text function:

The text is converted to lowercase: “seo services 2024! learn digital marketing & get the best results.”
The numbers are removed: “seo services! learn digital marketing & get the best results.”
The punctuation is removed: “seo services learn digital marketing get the best results”
The stopwords are removed: “seo services learn digital marketing best results”

The final cleaned

version would be: “seo services learn digital marketing best results”.

What is the Purpose of This Function?

The vectorize_text function is designed to transform text data into a numerical format that a machine learning model can process. Machine learning algorithms cannot interpret plain text directly—they require numeric representations to perform calculations and make predictions. This function bridges that gap by converting the cleaned text obtained from the previous preprocessing step into a structured format that algorithms can work with.

Specifically, the function uses the Bag of Words (BoW) approach. In this model, every unique word in the text is represented as a feature, and the text is transformed into a table showing the frequency of each word. Essentially, the BoW model counts how many times each word occurs in the document, producing a numerical vector that captures the text’s content in a format suitable for machine learning.

By converting text into vectors, the vectorize_text function enables the model to identify patterns, relationships, and important features within the data. This transformation is crucial for tasks such as text classification, sentiment analysis, and topic modeling. Without this step, the model would be unable to analyze or learn from the text effectively.

Step-by-Step Explanation

1. Define the Function:

Explanation:
- The function vectorize_text takes one input called preprocessed_content, which is a list of the cleaned text from all the webpages. This is the result from the previous step where we removed unnecessary words, symbols, and stopwords.
- The goal of this function is to convert this cleaned text into a matrix of word counts (numbers), so we can use it in further analysis or modeling.

2. Initialize a CountVectorizer Object:

Explanation:
- CountVectorizer is a tool from the scikit-learn library that helps convert text into numbers by counting how many times each word appears.
- We are creating a vectorizer object here, which will be used to transform the text into a matrix of word counts.

Let’s break down the parameters used in CountVectorizer:

max_df=0.9: This means we will ignore words that appear in more than 90% of the documents (webpages). If a word appears almost everywhere, it’s probably not very meaningful (e.g., “service” might appear on every page).
min_df=2: This means we will ignore words that appear in fewer than 2 documents. This helps remove rare or unusual words that don’t add much value to the analysis.
stop_words=’english’: This is an additional safety measure to remove common English stopwords like “the,” “and,” “is.” Even though we removed stopwords in the previous step, this ensures that any missed stopwords are removed.
Why this is important:
- The CountVectorizer tool transforms the text into a Bag of Words model, which is basically a big table showing how often each word appears in each document (webpage). This table is the numerical format that the machine learning model can work with.

3. Fit and Transform the Preprocessed Text:

· Explanation:

The fit_transform method does two things:
1. Fit: It looks at the preprocessed text and learns which unique words are present across all the webpages. It creates a vocabulary of these words.
2. Transform: It then counts how many times each word appears in each document (webpage) and puts that information into a matrix (a grid of numbers).
The result is a matrix where:

Each row represents a webpage.
Each column represents a unique word.
The numbers in the matrix show how many times a word appears on a particular webpage.

· Why this is important:

This step transforms the text into a format (numbers) that a machine learning model can work with. Without converting text to numbers, the model wouldn’t be able to understand or process it.

4. Return the Vectorizer and Word Matrix:

· Explanation:

The function returns two things:
1. vectorizer: This is the CountVectorizer object that knows the vocabulary (the list of words it found) and can be used later for further analysis.
2. word_matrix: This is the matrix of word counts for each document (webpage), which will be used in the next steps of the analysis.

· Why this is important:

The vectorizer object helps us keep track of the words (vocabulary), and the word_matrix is the actual data we need to perform further analysis, such as topic modeling or similarity calculations.

What Happens in This Step?

In this step, we are turning our cleaned text into numbers so that it can be used in machine learning models. Specifically, we are creating a Bag of Words model, which is like a big table where:

Each row is a webpage.
Each column is a word.
The numbers in the table represent how many times each word appears in each webpage.

This conversion is crucial because computers can only process numbers—not raw text—so the text has to be transformed into a numerical format before any further analysis can be done.

Example of How It Works:

Let’s say we have two cleaned documents (webpages):

“seo services digital marketing best results”
“learn digital marketing tips seo experts”

The Bag of Words model would look like this:

	best	digital	experts	learn	marketing	results	seo	services	tips
Doc1	1	1	0	0	1	1	1	1	0
Doc2	0	1	1	1	1	0	1	0	1

Rows: Each row represents a document (or webpage).
Columns: Each column represents a unique word that appears in the documents.
Numbers: The numbers in the table show how many times a word appears in each document.

For example:

The word “seo” appears 1 time in both Doc1 and Doc2.
The word “services” appears 1 time in Doc1 and 0 times in Doc2.

I’ve gone through each word of your question to ensure that I understand exactly what you’re asking. You want me to explain the create_topic_model function in very simple terms, so that even someone without a technical background can understand it.