Web Content Mining Concepts Techniques and a Comparative Methodology for Modern Web Data Extraction
Keywords:
Web Content Mining, Web Data Extraction, Text Mining, Natural Language Processing, Machine Learning, Web Mining Tools, Information ExtractionAbstract
Rapid development of the World Wide Web (WWW) has led to vast amount of data having diverse structures, both structured, semi-structured and unstructured formats. The challenge has grown critical in the research field to be able to extract meaning and implications from such data. 1.5 Web Content Mining is an important process of applying data mining techniques, text mining and artificial intelligence techniques to convert the unstructured web documents to structured knowledge. This paper summarizes the concepts, techniques and tools used for web content mining, discusses the most recent achievements (with the aid of machine learning and natural language processing). This study is different from the survey metric-based approaches in that it presents a comparative analysis of classical and AI based mining methods, and evaluates them according to their efficiency and their benefits of being accurate and scalable. To implement the proposed lightweight framework, three web content mining tools are evaluated that are widely used in the Web, namely Web Info Extractor, Mozenda and Screen Scraper. In general, the results have broad implications regarding the capabilities and limitations of the existing tools and the better performance and versatility of the new tools based on AI for extraction accuracy. The study results can be of value to the researchers and practitioners involved in building the effective Web content mining systems for the real-world applications like, e-commerce and Cyber security.
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