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DOI: 10.14569/IJACSA.2022.01312105
PDF

A Real-Time Open Public Sources Text Analysis System

Author 1: Chi Mai Nguyen
Author 2: Phat Trien Thai
Author 3: Van Tuan Nguyen
Author 4: Duy Khang Lam

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: With the emergence of digital newspapers and social media, one can easily suffer from information overload. The enormous amount of data they provide has created several new challenges for computational and data mining, especially in the natural language processing field. Many pieces of research focusing on the information extraction process, such as named entity recognition, entity linking, and text analysis methodologies, are available. However, there is a lack of development for a system to unify all these advanced techniques. The current state-of-the-art systems are either semi-automatic or can only handle short-text documents. Most of them are not real-time or have a long lag. Some of them are domain restricted. Many of them only focus on a single source: Twitter. In this work, we proposed a system that can automatically collect, extract, and analyze information from public source text documents, like news and tweets. The system can be used in different domains, such as scientific research, marketing, and security-related domains.

Keywords: Named entity recognition; entity linking; text analysis system; data mining; natural language processing

Chi Mai Nguyen, Phat Trien Thai, Van Tuan Nguyen and Duy Khang Lam, “A Real-Time Open Public Sources Text Analysis System” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01312105

@article{Nguyen2022,
title = {A Real-Time Open Public Sources Text Analysis System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01312105},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01312105},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Chi Mai Nguyen and Phat Trien Thai and Van Tuan Nguyen and Duy Khang Lam}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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