Editorial for the Special Issue on “Natural Language Processing and Text Mining”

Gamallo, Pablo; García, Marcos

doi:10.3390/info10090279

Cited by 2 publications

(4 citation statements)

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“…Forensic ontology is the backbone of the proposed information system, providing the rules and constructs governing how data is stored, retrieved, and analyzed. In addition to serving as a repository for multi-modal data, ontology plays an active role in the reasoning process [92][93][94]. By incorporating descriptive logic rules into the ontology, the system can conduct formal reasoning to generate new insights or hypotheses based on the existing data.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

“…In that case, the ontology-based reasoning engine can automatically identify potential matches or discrepancies with existing records, thereby aiding investigators in drawing timely and accurate conclusions. This proactive role of the ontology extends the system's utility beyond mere data storage, making it an invaluable tool for both reactive and proactive investigative processes [91,92,95,96].…”

Section: Conceptual Frameworkmentioning

confidence: 99%

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Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Spyropoulos,

Bratsas,

Makris

et al. 2023

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Nowadays, more and more sciences are involved in strengthening the work of law enforcement authorities. Scientific documentation is evidence highly respected by the courts in administering justice. As the involvement of science in solving crimes increases, so does human subjectivism, which often leads to wrong conclusions and, consequently, to bad judgments. From the above arises the need to create a single information system that will be fed with scientific evidence such as fingerprints, genetic material, digital data, forensic photographs, information from the forensic report, etc., and also investigative data such as information from witnesses’ statements, the apology of the accused, etc., from various crime scenes that will be able, through formal reasoning procedure, to conclude possible perpetrators. The present study examines a proposal for developing an information system that can be a basis for creating a forensic ontology—a semantic representation of the crime scene—through descriptive logic in the owl semantic language. The Interoperability-Enhanced information system to be developed could assist law enforcement authorities in solving crimes. At the same time, it would promote closer cooperation between academia, civil society, and state institutions by fostering a culture of engagement for the common good.

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Section: Conceptual Frameworkmentioning

confidence: 99%

Section: Conceptual Frameworkmentioning

confidence: 99%

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Spyropoulos,

Bratsas,

Makris

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…Thus, the massive increase in the amount of digital textual data has given rise to the generation of new insights and opportunities for research through new channels. Considering recent breakthroughs in natural language processing (NLP), machine learning, deep learning, and big data, text mining is now a more valuable and advantageous method for combining linguistic theories with NLP applications aiming to convert data from an unstructured format to a structured format (Gamallo & Garcia, 2019).…”

Section: Text Mining and Natural Language Processingmentioning

confidence: 99%

“…The core of NLP is to use linguistic concepts, for example, part-of-speech (PoS) tagging, lemmatization, and anaphora resolution. Meanwhile, text mining techniques do not have to understand fuller meaning representation; their aim is the extraction of items of knowledge or regular patterns across several documents (Gamallo & Garcia, 2019).…”

Section: Text Mining and Natural Language Processingmentioning

confidence: 99%

Management Responses to Online Reviews: Big Data From Social Media Platforms

Gokce¹

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User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided.

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Editorial for the Special Issue on “Natural Language Processing and Text Mining”

Abstract: Natural language processing (NLP) and Text Mining (TM) are a set of overlapping strategies working on unstructured text [...]

Cited by 2 publications

References 11 publications

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Management Responses to Online Reviews: Big Data From Social Media Platforms

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