Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection

The modern era, with information overload, has compelled strategic decision makers to obtain assistance from artificial intelligence- (AI) based decision support systems (DSS). Data-driven DSSs powered by powerful AI algorithms can instantly categorize unstructured data, assign context, and produce...

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Autores principales:	Fahim K. Sufi, Musleh Alsulami
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Anomaly detection decision support system entity detection media monitoring with explainable AI sentiment analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5c7cfa108bca4279b2baefcf09a5db8c
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id	oai:doaj.org-article:5c7cfa108bca4279b2baefcf09a5db8c
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spelling	oai:doaj.org-article:5c7cfa108bca4279b2baefcf09a5db8c2021-11-20T00:01:00ZAutomated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection2169-353610.1109/ACCESS.2021.3127571https://doaj.org/article/5c7cfa108bca4279b2baefcf09a5db8c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612169/https://doaj.org/toc/2169-3536The modern era, with information overload, has compelled strategic decision makers to obtain assistance from artificial intelligence- (AI) based decision support systems (DSS). Data-driven DSSs powered by powerful AI algorithms can instantly categorize unstructured data, assign context, and produce meaningful insights from such data. This paper presents an automated media monitoring system that can analyze unstructured global events reported in online news, government websites, and major social media to produce significant insights with explainable AI. Using this innovative system, a decision maker can focus on global events requiring urgent attentions since it segregates millions of unnecessary data by using the presented methodology involving entity detection, sentiment analysis, and anomaly detection. The system was designed, deployed, and tested during June 2, 2021 – September 1, 2021. During this 92-day period, the system connected to 2,397 distinct types of news sources and automatically fetched 22,425 major event descriptions from 192 countries. Then, to assign meaning and context to the unstructured event descriptions, the system performed AI-based entity detection and sentiment analysis of these global events. The proposed system is sufficiently robust to detect anomalies instantly from <inline-formula> <tex-math notation="LaTeX">$2.76\times {10}^{8404}$ </tex-math></inline-formula> possible scenarios and provides detailed explanations by using natural language descriptions along with dynamic line charts and bar charts to portray detailed reasoning. The entity detection algorithm had a F1-score of 0.994 and the anomaly detection algorithm had an area under curve score of 0.941, establishing the proposed system with explainable AI as the most accurate, robust media monitoring system according to the literature.Fahim K. SufiMusleh AlsulamiIEEEarticleAnomaly detectiondecision support systementity detectionmedia monitoring with explainable AIsentiment analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152449-152460 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anomaly detection decision support system entity detection media monitoring with explainable AI sentiment analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Anomaly detection decision support system entity detection media monitoring with explainable AI sentiment analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Fahim K. Sufi Musleh Alsulami Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
description	The modern era, with information overload, has compelled strategic decision makers to obtain assistance from artificial intelligence- (AI) based decision support systems (DSS). Data-driven DSSs powered by powerful AI algorithms can instantly categorize unstructured data, assign context, and produce meaningful insights from such data. This paper presents an automated media monitoring system that can analyze unstructured global events reported in online news, government websites, and major social media to produce significant insights with explainable AI. Using this innovative system, a decision maker can focus on global events requiring urgent attentions since it segregates millions of unnecessary data by using the presented methodology involving entity detection, sentiment analysis, and anomaly detection. The system was designed, deployed, and tested during June 2, 2021 – September 1, 2021. During this 92-day period, the system connected to 2,397 distinct types of news sources and automatically fetched 22,425 major event descriptions from 192 countries. Then, to assign meaning and context to the unstructured event descriptions, the system performed AI-based entity detection and sentiment analysis of these global events. The proposed system is sufficiently robust to detect anomalies instantly from <inline-formula> <tex-math notation="LaTeX">$2.76\times {10}^{8404}$ </tex-math></inline-formula> possible scenarios and provides detailed explanations by using natural language descriptions along with dynamic line charts and bar charts to portray detailed reasoning. The entity detection algorithm had a F1-score of 0.994 and the anomaly detection algorithm had an area under curve score of 0.941, establishing the proposed system with explainable AI as the most accurate, robust media monitoring system according to the literature.
format	article
author	Fahim K. Sufi Musleh Alsulami
author_facet	Fahim K. Sufi Musleh Alsulami
author_sort	Fahim K. Sufi
title	Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
title_short	Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
title_full	Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
title_fullStr	Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
title_full_unstemmed	Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection
title_sort	automated multidimensional analysis of global events with entity detection, sentiment analysis and anomaly detection
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5c7cfa108bca4279b2baefcf09a5db8c
work_keys_str_mv	AT fahimksufi automatedmultidimensionalanalysisofglobaleventswithentitydetectionsentimentanalysisandanomalydetection AT muslehalsulami automatedmultidimensionalanalysisofglobaleventswithentitydetectionsentimentanalysisandanomalydetection
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Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection

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