A Real-Time Open Public Sources Text Analysis System
Chi Mai Nguyen, P. Kamakshi Thai, Van‐Dinh Nguyen, Duy Khang Lam · International Journal of Advanced Computer Science and Applications · 2022
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.