Abstract
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 $2.76\times {10}^{8404}$ 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.
Highlights
With the advent of online news portals, e-governance and social media, millions of global events are generated on a regular basis [1]
According to the findings reported in the literature and to the best of our knowledge, the proposed system provides the highest quality of robustness along with a high level of accuracy, for detecting abnormal global events with seamless integration and explainable artificial intelligence (AI)
Our media aggregation engine allows us to connect and automatically receive event updates from a wide range of online media through dedicated application programming interfaces (APIs) and web scraping technologies to collate new events or event updates into our global event database
Summary
With the advent of online news portals, e-governance and social media, millions of global events are generated on a regular basis [1]. Despite the importance of conducting an indepth analysis of the sentiments toward global events, most of the mainstream literature has obtained their media message only from one or two media sources, as Table I shows. These studies mainly depended on a single source of information, such as Twitter [10] [9] [20] [21], Facebook [22], Instagram [23], Amazon reviews [24], and online blogs [6] [25], for conducting sentiment analysis research. Very few sources used website portals of online news agencies such as BBC, New York Times, and Summary of World Broadcast [17] [26] [27]
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