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This type of data requires special attention in various industries. For instance, in the travel and hospitality sector, businesses monitor customer reviews, travel forums, and social media to assess service quality, satisfaction, and emerging trends. In public safety, national security, and biosurveillance, monitoring online forums, social media, and news outlets is essential for identifying threats, criminal activity, or public safety concerns. Additionally, the challenge of efficiently monitoring unstructured text streams, such as business emails, is a significant issue for large organizations. In this paper, we propose a method that combines natural language processing and text visualization techniques with traditional process monitoring algorithms to enhance the analysis and understanding of text streams. Our method involves mapping text streams onto a bivariate plot, followed by the application of monitoring techniques on a sequence of statistics derived from these plots. This sequential analysis reveals valuable insights into temporal patterns and fluctuations. We integrate, in the proposed method, two robust real-time monitoring procedures\u2014Cumulative Sum and Pruned Exact Linear Time\u2014both widely used for change point detection, allowing the framework to support also retrospective analysis for extracting critical insights. The effectiveness of this framework is demonstrated through simulations and two real-world case studies. The first case study involves the BBC, where the framework is used to detect extreme events through published articles. The second, provided by a shipbroker in Greece, applies the framework to monitor a large volume of emails. Ultimately, the proposed solution provides a comprehensive approach to dynamically monitoring time-varying unstructured text streams, offering organizations a powerful tool for informed decision-making and improved market intelligence.<\/jats:p>","DOI":"10.1007\/s41060-025-00750-x","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T18:05:27Z","timestamp":1743530727000},"page":"4757-4776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-time monitoring of streaming text data by integrating text visualization techniques and natural language processing"],"prefix":"10.1007","volume":"20","author":[{"given":"Grigorios","family":"Papageorgiou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sotirios","family":"Bersimis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Polychronis","family":"Economou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"issue":"4","key":"750_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1097\/NCN.0b013e3181a91b58","volume":"27","author":"S Hyun","year":"2009","unstructured":"Hyun, S., Johnson, S.B., Bakken, S.: Exploring the ability of natural language processing to extract data from nursing narratives. 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