Multiclass Web Content and URL Classification Index

Categorising the vast expanse of the internet requires structured information that bridges the gap between raw web content and meaningful classifications. This collection provides text content and associated URLs for thousands of websites, with entries assigned to nine distinct sectors. By offering unprocessed text extracted directly from various online sources, it allows researchers to develop custom cleaning pipelines and evaluate the effectiveness of diverse natural language processing (NLP) techniques in a real-world context. The records offer a substantial foundation for understanding how textual signatures vary across different industries and creative fields. Classifying raw web text is like sorting an enormous, unorganised library where the books have no covers; you must read the actual pages to decide which shelf they belong on.

The information is delivered in a single CSV file titled classification-test.csv with a file size of 5.33 MB. It consists of approximately 13,500 records in total, with detailed validation checks showing 9,347 entries with high integrity for category and URL fields. As a static reference intended for classification testing and model benchmarking, the update frequency is set to never.

This resource is ideal for training and evaluating multi-class classification models within the domain of web content analysis. It is well-suited for natural language processing tasks, including topic modelling, keyword extraction, and the development of custom preprocessing algorithms. Additionally, developers can use these records to build automated website tagging systems or to improve the accuracy of web filtering and search indexing tools by analysing the raw textual patterns of different site categories.

The scope of the data encompasses 13,500 labelled websites spanning nine unique categories. The most prominent sectors represented in the validated subset are Arts & Design (39%) and Scientific & Technical Services (20%). The data captures a variety of web content in its raw form, including instances where sites were not found or content was not acceptable, providing a realistic snapshot of digital information across a wide range of professional, scientific, and creative industries.

CC0: Public Domain

Data scientists and machine learning engineers can leverage these records to refine text classification algorithms and feature extraction methods. Web developers might utilise the collection to train intelligent systems for automatic content categorisation. Furthermore, students and academic researchers can find this a valuable primary source for studying the nuances of web-based language and the specific challenges involved in processing uncleaned HTML text.

Original Data Source: Multiclass Web Content and URL Classification Index