I need to categorised websites, which tools its work for you?

Hello, I am creating a link-building database, and we want to create our own categories for each website. However, we are facing issues with the large quantity of categorization. Do you have any advice on how to automate and simplify the categorization process?

Create an excel with 3 columns
Site category1 category 2
Category1 is your large quantity categories

Now decide you want 50 categories or 100 or 10 that will your final categories - category2

Then ask chatgpt to categorize category1 into 10, 50 100 how many you want.

You can use AI tools like chatGPT to look at the website content and automatically assign categories based on keywords and context

For categorizing 60 websites, you can use:

1. Google Sheets/Excel: Manually input and organize websites into categories.
2. SEMrush/SimilarWeb: Automated categorization based on traffic and industry.
3. Clearbit API: Classifies websites by industry.
4. Google Cloud NLP API: Analyzes and categorizes websites based on content.

These tools should efficiently help you categorize and organize your websites.

Karl_b said:

Hello, I am creating a link-building database, and we want to create our own categories for each website. However, we are facing issues with the large quantity of categorization. Do you have any advice on how to automate and simplify the categorization process?

Click to expand...

Depending on the amount of urls/domains you want to categorise, you can try uClassify: https://www.uclassify.com/pricing

You can use tools like SimilarWeb, SEMrush, and Ahrefs for categorising websites for industry-based categorisation. Google Cloud NLP and IBM Watson provide AI-driven categorization by analyzing content. BeautifulSoup or Scrapy allows for custom web scraping. For large-scale projects, FastText or BERT models can classify websites based on content. Tools like Trello and Airtable help organize and manually tag sites.

Natural Language Processing (NLP)-based automation: Use Python with scikit-learn or spaCy to analyze website content (e.g., meta descriptions, titles) and categorize via text classification or topic modeling (e.g., LDA). Train with a small, manually labeled dataset to ensure accuracy.
Web Scraping + Rules: Use BeautifulSoup or Scrapy and apply rule-based classification (e.g., "health" in the URL → "health" category). Use pandas.
Insights API: Use Clearbit, SimilarWeb, or the Google Cloud Natural Language API to extract pre-existing categories or metadata to reduce manual work.
Clustering: Apply K-means or DBSCAN to group similar websites based on content or backlinks, then label clusters as categories.
Hybrid Workflow: Automate initial categorization, then use Airtable or Google Sheets for team review and refinement. Feedback corrections to improve the model.
SEO Tools: Export pre-categorized data from Moz, Ahrefs, or SEMrush to integrate into your database.