
The explosive growth of news and news content generated worldwide, coupled with the expansion through online media and rapid access to data, has made trouble and screening of news tedious. An expanding need for a model that can reprocess, break down, and order main content to extract interpretable information, explicitly recognizing subjects and content-driven groupings of articles. This paper proposed automated analyzing heterogeneous news through complex event processing (CEP) and machine learning (ML) algorithms. Initially, news content streamed using Apache Kafka, stored in Apache Druid, and further processed by a blend of natural language processing (NLP) and unsupervised machine learning (ML) techniques.
FOS: Computer and information sciences, Artificial intelligence, Deep Web, Web Data Extraction, Trajectory Data Mining and Analysis, Mathematical analysis, Quantum mechanics, Web Data Extraction and Crawling Techniques, Cluster analysis, Artificial Intelligence, Machine learning, FOS: Mathematics, Information retrieval, Content Adaptation, Content (measure theory), Event (particle physics), Data mining, Web Crawling, Natural language processing, Physics, Computer science, Automatic Keyword Extraction from Textual Data, Top-k Query Processing, Streaming data, Computer Science, Physical Sciences, Signal Processing, Mathematics, Information Systems
FOS: Computer and information sciences, Artificial intelligence, Deep Web, Web Data Extraction, Trajectory Data Mining and Analysis, Mathematical analysis, Quantum mechanics, Web Data Extraction and Crawling Techniques, Cluster analysis, Artificial Intelligence, Machine learning, FOS: Mathematics, Information retrieval, Content Adaptation, Content (measure theory), Event (particle physics), Data mining, Web Crawling, Natural language processing, Physics, Computer science, Automatic Keyword Extraction from Textual Data, Top-k Query Processing, Streaming data, Computer Science, Physical Sciences, Signal Processing, Mathematics, Information Systems
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
