Text and web content mining : a systematic review

Almatrooshi, F ORCID: https://orcid.org/0000-0001-9446-273X, Alhammadi, S ORCID: https://orcid.org/0000-0002-0586-1403, Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 and Shaalan, K ORCID: https://orcid.org/0000-0003-0823-8390 2022, Text and web content mining : a systematic review , in: International Conference on Emerging Technologies and Intelligent Systems (ICETIS), 25th-26th June 2021, Al Buraimi, Oman.

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Abstract

Text and content mining are the subcategory of data mining. This category of data mining is used to extract the information from web or web pages of a website. This mining identifies useful information from web content like web pages, search logs, and other website-related content. The extracted information can be used in many applications, for example, we can extract opinions from online sources and web hierarchy which provides better insights and knowledge. In this paper, we conducted a systematic review that included 18 research papers that are relevant to the topic and matches the inclusion criteria of this study. From these research papers we were able to answer the research questions that we identified. The questions are related to the applications, techniques and issues of the text and web mining. The findings suggest that many research papers made a good foundation for this topic, and gave an informative explanation of each type of techniques used in text and web mining, as well as some issues that can be a future work for researchers who are interested in the topic.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: ICETIS 2021: Proceedings of International Conference on Emerging Technologies and Intelligent Systems
Publisher: Springer
Series Name: Lecture Notes in Networks and Systems
ISBN: 9783030826154 (paperback); 9783030826161 (ebook)
ISSN: 2367-3370
Related URLs:
Depositing User: USIR Admin
Date Deposited: 30 Nov 2021 09:53
Last Modified: 30 Nov 2021 09:53
URI: http://usir.salford.ac.uk/id/eprint/62443

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