Система автоматизованого аналізу серверних логів із застосуванням методів NLP та алгоритмів стиснення текстової інформації

Objective - This paper focuses on analyzing the significance of sports footwear brands by processing large text data from the Internet. In a modern environment, the brand distinguishes a company's products or services from those of its competitors. A strong brand can help build trust with customers as they perceive the brand as reliable and trustworthy. Methodology/Technique - The study uses NLP (Natural Language Processing) methods to analyze rich text content on the Internet. The research focus is based on the application of innovative methods to determine the importance and value of a brand using NLP techniques by analyzing the content of a large corpus of text originating from websites dealing with sports footwear brands. The NLP analysis models were programmed using the low-code analysis tool KNIME. Findings – The analysis is carried out for the most well-known sports footwear brands such as Nike, Adidas, Puma, Under Armour, Reebok and Asics. The research object refers to the analysis of brand significance and the evaluation of consumer opinions on sports footwear, based on the processing of large text data from the internet. Novelty - The research results are based on an innovative approach to measuring and evaluating the brand significance in sports footwear using NLP methods to analyze large text content from the Internet. The results obtained show that this new approach to metrics and evaluation can significantly improve existing methods of brand evaluation. Type of Paper: Empirical JEL Classification: M32, M39. Keywords: Brand, NLP Method, Text Analysis, Online Brand Management Strategies, Sports Footwear. Reference to this paper should be made as follows: Tarade, S.S; Vuković, D. (2023). Discovering the Significance of Sports Footwear Brands through Text Analysis, J. Mgt. Mkt. Review, 8(4), 166 – 175. https://doi.org/10.35609/jmmr.2023.8.4(7)

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-terminal symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy

Urban planners routinely engage with extensive textual materials, such as zoning codes, comprehensive plans, and public comments. The large volume of textual data being generated in cities offers new opportunities to use emerging computational technologies to process and analyze textual data. In this research, we compare how natural language processing (NLP) techniques and large language models (LLMs) compare to human qualitative coding techniques to identify public sentiment and topics contained in public comments about the Minneapolis 2040 upzoning. We use a custom rubric developed in collaboration with urban planners to assess outputs across these different methods, scoring outputs on factors such as accuracy, convergence, creativity, efficiency, and interpretability. Additionally, we conduct interviews with practicing urban planners to understand their perceptions of integrating these computational techniques into their existing workflows. We find that using NLP techniques are helpful in providing urban planners with an aerial view of their data, but require additional human interpretation. In contrast, using LLMs markedly improves efficiency, interpretability, and descriptiveness over traditional NLP techniques, but requires human validation to address concerns related to social biases and equity. We further find that urban planners are open to using new text processing technologies, but have reservations about entirely outsourcing decision-making to AI tools, viewing AI technologies more as “co-pilots” rather than autonomous agents. Our findings underscore the importance of integrating human judgment into using computational tools to develop a more informed, equitable, and reflective practice in an era of expanding urban data and computational technologies.

A novel algorithm for text compression is developed which consists of two parts. In the first part the text sample is encoded by the Ziv-Lempel asymptotic structured codebook. In the second part decoding is carried out by a novel rule based (RB) algorithm which improves the compression ratio for a given text sample by up to 50% when compared to the full text search as required by the Ziv-Lempel decoding algorithm.

Text compression methods can be divided into two classes: symbolwise and parsing . Symbolwise methods assign codes to individual symbols, while parsing methods assign codes to groups of consecutive symbols (phrases). The set of phrases available to a parsing method is referred to as a dictionary . The vast majority of parsing methods in the literature use greedy parsing (including nearly all variations of the popular Ziv-Lempel methods). When greedy parsing is used, the coder processes a string from left to right, at each step encoding as many symbols as possible with a phrase from the dictionary. This parsing strategy is not optimal, but an optimal method cannot guarantee a bounded coding delay. An important problem in compression research has been to establish the relationship between symbolwise methods and parsing methods. This paper extends prior work that shows that there are symbolwise methods that simulate a subset of greedy parsing methods. We provide a more general algorithm that takes any nonadaptive greedy parsing method and constructs a symbolwise method that achieves exactly the same compression. Combined with the existence of symbolwise equivalents for two of the most significant adaptive parsing methods, this result gives added weight to the idea that research aimed at increasing compression should concentrate on symbolwise methods, while parsing methods should be chosen for speed or temporary storage considerations.

The article examines a text compression algorithm. The research objective involves clarifying principles of the text compression process. To achieve this research objective, the author conducts an experiment during which a corpus of the English-language articles and reviews of the subject area “Shipbuilding” has been developed. Scientific originality of the study lies in the fact that the researcher for the first time discovers principles of professional vocabulary usage in scientific articles and scientific reviews. A statistical analysis allows concluding that in scientific reviews, the text compression algorithm is based on using complicated multicomponent terms. In scientific articles, two- or three-component terms prevail.

Network log data is significant for network administrators, since it contains information on every event that occurs in a network, including system errors, alerts, and packets sending statuses. Effectively analyzing large volumes of diverse log data brings opportunities to identify issues before they become problems and to prevent future cyberattacks; however, processing of the diverse NetFlow data poses challenges such as volume, velocity, and veracity of log data. In this study, by means of Elasticsearch, Logstash, and Kibana, i.e., the ELK Stack, we construct an analysis and management system for network log data, which provides functions to filter, analyze, and display network log data for further applications and creates data visualization on a Web browser. In addition, an advanced cyberattack detection model is facilitated using deep neural network (DNN), recurrent neural networks (RNN), and long short-term memory (LSTM) approaches. By knowing cyberattack behaviors and cross-validating with the log analysis system, one can learn from this model the characteristics of a variety of cyberattacks. Finally, we also implement Grafana to perform metrics monitoring.

The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.

BackgroundQualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure.ObjectiveThe purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods.MethodsWe conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis.ResultsThe qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions.ConclusionsNLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context.

Climate change will most likely lead to an increase of extreme weather events, including heavy rainfall with soil surface runoff and erosion. Adapting agricultural management practices that lead to increased infiltration capacities of soil has potential to mitigate these risks. However, effects of agricultural management practices (tillage, cover crops, amendment, …) on soil variables (hydraulic conductivity, aggregate stability, …) often depend on the pedo-climatic context. Hence, the only possibility to gather information needed to advise stakeholders on suitable management practices is to quantify such dependencies using meta-analyses of studies investigating this topic. As a first step, structured information from scientific publications needs to be extracted to build a meta-database, which then can be analyzed and recommendations can be given in dependence to the pedo-climatic context. Manually building such a database by going through all publications is very time-consuming. Given the increasing amount of literature, this task is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, two corpora of documents were used, which we refer to as the OTIM and the Meta corpus in the following. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (https://github.com/climasoma/otim-db). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. We focused on three NLP techniques: topic modeling, tailored regular expressions and dictionaries and the shortest dependency path. We used topic modeling to sort the individual source-publications of the Meta corpus into 6 topics (e.g. related to cover crops, biochar, …) with a coherence metric Cv ranging from 0.7 to 0.9; Then, we used tailored regular expressions and dictionaries to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions on the OTIM corpus. We found that the respective information could be retrieved with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of practices keywords (e.g. ‘biochar’, ‘zero tillage’, …) and soil variables (e.g. ‘soil aggregate’, ‘hydraulic conductivity’, ‘crop yield’,…) from the source-publications’ abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and soil property. This quickly provided an overview of the different driver-variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks such as selecting relevant publications on a topic, extracting specific information to build databases for meta-analysis and providing an overview of relationships found in the corpus. While human supervision remains essential, NLP methods have the potential to support fully automated evidence synthesis that can be continuously updated as new publications become available.

BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practiceMethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change will most likely lead to an increase of extreme weather events, including heavy rainfall with soil surface runoff and erosion. Adapting agricultural management practices that lead to increased infiltration capacities of soil has potential to mitigate these risks. However, effects of agricultural management practices (tillage, cover crops, amendment, &hellip;) on soil variables (hydraulic conductivity, aggregate stability, &hellip;) often depend on the pedo-climatic context. Hence, the only possibility to gather information needed to advise stakeholders on suitable management practices is to quantify such dependencies using meta-analyses of studies investigating this topic. As a first step, structured information from scientific publications needs to be extracted to build a meta-database, which then can be analyzed and recommendations can be given in dependence to the pedo-climatic context. Manually building such a database by going through all publications is very time-consuming. Given the increasing amount of literature, this task is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, two corpora of documents were used, which we refer to as the OTIM and the Meta corpus in the following. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (<a href="https://github.com/climasoma/otim-db" target="_blank" rel="noopener">https://github.com/climasoma/otim-db</a>). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. We focused on three NLP techniques: topic modeling, tailored regular expressions and dictionaries and the shortest dependency path. We used topic modeling to sort the individual source-publications of the Meta corpus into 6 topics (e.g. related to cover crops, biochar, &hellip;) with a coherence metric Cv ranging from 0.7 to 0.9; Then, we used tailored regular expressions and dictionaries to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions on the OTIM corpus. We found that the respective information could be retrieved with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of practices keywords (e.g. &lsquo;biochar&rsquo;, &lsquo;zero tillage&rsquo;, &hellip;) and soil variables (e.g. &lsquo;soil aggregate&rsquo;, &lsquo;hydraulic conductivity&rsquo;, &lsquo;crop yield&rsquo;,&hellip;) from the source-publications&rsquo; abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and soil property. This quickly provided an overview of the different driver-variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks such as selecting relevant publications on a topic, extracting specific information to build databases for meta-analysis and providing an overview of relationships found in the corpus. While human supervision remains essential, NLP methods have the potential to support fully automated evidence synthesis that can be continuously updated as new publications become available.

Purpose This study examines the evolution of education research in Kazakhstan from 1992 to 2024, a period marked by profound transformation, aiming to assess how scholarly output aligns with national education policy and broader developmental priorities. Design/methodology/approach A mixed bibliometric and natural language processing (NLP) approach was employed to analyze 956 English-language publications indexed in the Web of Science. The study combined quantitative analyses of publication trends, authorship and institutional collaboration with NLP techniques, including keyword co-occurrence, topic modeling and sentiment analysis, framed by a review of Kazakhstan’s education policy phases: post-Soviet foundational reforms and an era of global alignment. Findings Results reveal a substantial surge in research output, particularly after 2020, driven by a core set of highly productive authors and institutions. Thematic analyses indicate partial alignment with policy priorities, with multilingual education, digitalization and pedagogical innovation prominent, while inclusive education and social justice appear less substantively addressed. Collaboration networks display a core-periphery structure, with Nazarbayev University acting as the primary international and national hub. Originality/value This study offers the first comprehensive, longitudinal mapping of Kazakhstani education research, integrating bibliometric and NLP methods with policy analysis. It highlights both the rapid quantitative growth and the structural and thematic limitations of the field, providing a foundation for policy and institutional strategies aimed at fostering more diverse, critical and sustainable research.

As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages natural language processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit with those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides in the cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.