Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study

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.

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.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

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.

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.

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

Aircraft maintenance reliability analysis relies on accurate defect categorisation, typically encoded with Joint Aircraft System Component (JASC) aircraft codes. However, the manual assignment of these codes is prone to human error, or incompleteness, which can impact fleet analysis and subsequent decision-making. This paper addresses automating JASC code assignment by applying Natural Language Processing (NLP) to a representative open-source dataset of defect reports. Two NLP approaches are applied to predict JASC codes from unstructured defect description, followed by a comparison of classification performance. In doing so, the contributions of this paper are two-fold: i) presenting a novel application of LLMs to automate defect categorisation with minimal domain expertise, and ii) providing a quantitative comparative analysis of legacy NLP and Large Language Model (LLM)-based methods. By integrating aviation expertise with computer science and software engineering, such as NLP and machine learning techniques, this transdisciplinary research has the potential to deliver practical solutions to end-users. Results on application to the Federal Aviation Administration (FAA) Service Difficulty Reporting System (SDRS) dataset demonstrate promising accuracy for both models on a subset of the data. However, challenges remain regarding model explainability for decision-making as well as data imbalance considerations.

This paper presents a comprehensive comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats. Traditional machine learning models with embeddings, large language models (LLMs) with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation (RAG) approaches are evaluated. The evaluation framework assesses three critical dimensions: classification accuracy, processing speed, and computational costs. A hybrid moderation system architecture is proposed that optimizes human moderator workload through automated detection and incorporates continuous learning mechanisms. The experimental results demonstrate significant performance variations across methods, with fine-tuned DistilBERT achieving optimal accuracy-cost trade-offs. The findings provide empirical evidence for deploying cost-effective, efficient content moderation systems in dynamic online gaming environments.

The paper presents a comprehensive Natural Language Processing (NLP) evaluation framework for online public opinion analysis, addressing the limitations of singular NLP techniques. It highlights the significance of public opinion analysis in the era of big data and reviews commonly employed NLP methods. The proposed framework offers universality, comprehensiveness, and flexibility, aiding managers and users in better understanding product sentiments and purchasing behaviors for informed decision-making. The proposed NLP evaluation framework includes five stages, Data Collection, Qualitative Analysis, Sentiment Analysis, Semantic Relationship Mining, and Comprehensive Analysis. It involves techniques like Sentiment Analysis, Topic Modeling, and Co-Occurrence Networks to analyze sentiment and identify semantic relationships. Our case study on nursing care food market validates the framework's feasibility, proposing enhancements in taste, gifting nature, pricing, and product strategy. Future research will expand its applicability across domains and refine inter-stage connections while maintaining flexibility.

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

A long tradition in innovation research asserts that data-driven organizations excel at incremental innovation, but allocate resources away from less-measurable breakthrough innovations. Questioning this premise, I distinguish the magnitude of an organization’s use of quantitative analysis from the epistemic plasticity of its organizational culture (the extent to which its members use different kinds of analyses). I argue that organizations using more quantitative analysis will actually produce more breakthrough innovations—provided that their cultures are plastic enough to use qualitative analysis liberally as well. To test my theory, I measure innovation performance using product-level sales and attribute data for over 3,500 consumer product launches from 61 organizations between 2010 and 2016; I measure use of qualitative and quantitative analyses using natural language processing on employee résumés. I find that increased reliance on quantitative analysis decreases innovation performance when qualitative analysis is low, and, conversely, increases when qualitative analysis is high. Additional analyses show that this relationship is particularly strong for novel products, and in markets characterized by high uncertainty. I also explore antecedents: management fads, not organizational learning, appear to account for excessively data-driven cultures. The paper contributes to organizational theories of innovation, and to research linking organizational culture to strategic performance.

Traditional qualitative data analysis software, while greatly facilitating text analysis, remains entrenched in a tradition of semantic coding. Such an approach, being both time-consuming and poor at incorporating quantitative variables, is seldom applied to large-scale survey and database research (especially within the commercial sector). Lexical analysis, on the other hand, with its origins in linguistics and information technology, offers a more rapid solution for the treatment of texts, capable not only of identifying semantic content but also important structural aspects of language. The automatic calculation of word lexicons and rapid identification of relevant text fragments require an integration of both powerful quantitative analysis and search-and-retrieval tools. Lexical analysis is thus the ideal tool for the exploitation of open-text responses in surveys, offering a bridge between quantitative and qualitative analyses, opening new avenues for research and presentation of textual data.