Topic Modeling Techniques Research Articles

Using Topic Modeling to Understand Patients' and Caregivers' Perspectives About Left Ventricular Assist Device: Thematic Analysis.

Heart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers. We aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website. Using the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023. This study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management. These findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

Topic Modeling of the 2024 Election Using the BERTopic Method on Detik.com News Articles

In 2024, Indonesia will hold simultaneous general elections dominated by the participation of young people, especially Generation Z and Millennials, who seek political information primarily through the Internet, highlighting the crucial role of digital media in shaping public opinion. Detik.com is actively reporting on the 2024 elections, evidenced by a special election subchannel. However, the lack of topic categorization in this subchannel makes it difficult for readers to find in-depth information, and tracking and analyzing the large volume of news articles published daily is a significant challenge. This study employs Topic Modelling techniques, specifically the BERTopic method, to analyze topics related to the 2024 elections from Kompas.com news articles. The dataset, sourced from the detik.com election sub-channel, was collected via scraping from September 1, 2023, to February 14, 2024, totalling 15,019 articles. The text preprocessing involves case folding, cleaning, tokenizing, and stopword removal. Topic modelling using BERTopic includes embeddings with sentence-transformers "distiluse-base-multilingual-cased-v1," dimensionality reduction with UMAP, clustering with K-Means using optimal k=5 value evaluated by Elbow, tokenizer with CountVectorizer, and weighting scheme using c-TF-IDF. Based on the Silhouette Score of 0.566 and Silhouette plot results, the clustering results using the K-Means model with a value of k equal to 5 produce good clustering with clear inter-cluster distances. For other evaluations, the SSE value of 70223.257 provides an overview of the cluster distribution, the Davies-Bouldin Index of 0.758 shows that the cluster has a relatively good level of inter-cluster separation with good closeness within the cluster, the Calinski-Harabasz Index of 20083.489 shows good and compact inter-cluster separation, and the Dunn Index of 0.003 shows outliers that cause overlapping clusters and lack of clear separation. The evaluation results show that implementing the K-Means model with a value of k equal to 5 again emphasizes that the clustering results are good. The modelling results show an average topic coherence value of 0.0902 and produce five main topics in the 2024 election news on Detik.com topic 0: about presidential and vice presidential candidates (5,215 articles) with the representation of the words 'ganjar', 'prabowo', 'anies' and 'imin', topic 1: about general elections and related surveys (3,191 articles) with the representation of the words '2024', 'pemilu', 'pilpres' and 'suara', topic 2: news about Joko Widodo President (2,604 articles), topic 3: news about presidential and vice presidential debates (2046 articles) with representations of the words 'presiden', 'jokowi', 'demokrat' and 'politik' and topic 4: news about the figure Gibran Rakabuming Raka and related issues (1963 articles) with representations of the words 'raka', 'rakabuming', 'nomor' and 'urut'. Using the results of this research, readers can gain insights into the most discussed issues and the attention given to key figures in the 2024 election news on the detik.com news portal.

TopicRefiner: Coherence-Guided Steerable LDA for Visual Topic Enhancement.

This article presents a new Human-steerable Topic Modeling (HSTM) technique. Unlike existing techniques commonly relying on matrix decomposition-based topic models, we extend LDA as the fundamental component for extracting topics. LDA's high popularity and technical characteristics, such as better topic quality and no need to cherry-pick terms to construct the document-term matrix, ensure better applicability. Our research revolves around two inherent limitations of LDA. First, the principle of LDA is complex. Its calculation process is stochastic and difficult to control. We thus give a weighting method to incorporate users' refinements into the Gibbs sampling to control LDA. Second, LDA often runs on a corpus with massive terms and documents, forming a vast search space for users to find semantically relevant or irrelevant objects. We thus design a visual editing framework based on the coherence metric, proven to be the most consistent with human perception in assessing topic quality, to guide users' interactive refinements. Cases on two open real-world datasets, participants' performance in a user study, and quantitative experiment results demonstrate the usability and effectiveness of the proposed technique.

Information Security Research in the Information Systems Discipline: A Thematic Review and Future Research Directions

Information security continues to grow in importance in all aspects of society and therefore evolves as a prevalent research area. The information systems (IS) discipline offers a unique perspective to move this stream of literature forward. Using a semiautomated thematic analysis approach based on the topic modeling technique, we review a broad range of information security literature to investigate how we may integrate and advance our understanding of information security. Our analysis reveals four major themes, including information security policy (ISP) compliance, motivations and susceptibility, software security decisions, and firm security strategy. We also identify a theme of security in broader contexts, in which studies consider the societal impacts of information security and online hacker behavior. This review contributes to IS literature by 1) synthesizing the broad range of information security research published in the top journals of the field, moving beyond prior reviews focusing on a narrower scope such as individual ISP compliance; 2) identifying major themes and proposing an overarching research framework encompassing these themes and their interconnections, allowing us to envision future research directions; and 3) enumerating a semiautomated topic modeling approach that other researchers can employ.

Mapping the Science World with Correlated Topic Modeling Analysis from Science-Related Posts on the Reddit Platform

The main source of technology in today's world is science. The world of science is being reshaped every day with the continuation of studies in the fields of science and technology. However, the development of technology causes the amount of data that cannot be processed with traditional methods on technological platforms to increase day by day. Making meaningful data meaningful by processing unmeaningful data will enable companies, institutions, and organizations to transform big data into highly beneficial tools. One of the most effective data mining techniques for processing and making sense of data is topic modeling. In this study, semantic content analysis of science-related posts on the Reddit platform was conducted using corelated topic modeling (CTM), one of the topic modeling techniques. In the first nine months of 2022, the hidden meanings in Reddit posts and the correlation between these meanings were revealed, and the relevant results were shared. It is thought that the results obtained will be a source of ideas for people interested in science and scientists for their research.

Experiences of Alzheimer's disease and related dementia family caregivers on Reddit communities: A topic modeling and sentiment analysis.

Alzheimer's disease and related dementias (ADRD) are a spectrum of disorders characterized by cognitive decline, which pose significant challenges for both affected individuals and their caregivers. Previous literature has focused on patient family surveys which do not always capture the breadth of authentic experiences of the caregiver. Online social media platforms provide a space for individuals to share their experiences and obtain advice toward caring for those with ADRD. This study leverages Reddit, a platform frequented by caregivers seeking advice for caring for a family member with advice for ADRD. To identify the topics of discussion or advice that most caregivers seek and sought after, we employed structured topic modeling techniques such as BERTopic to analyze the content of these posts and use an intertopic distance map to discern the variation in themes across different Reddit categories. In addition, we analyze the sentiment of the Reddit postings using Valence Aware Dictionary and Sentiment Reasoner to deduce the degree of negative, positive, and neutral sentiment of the discussion posts. Our findings reveal that the topics that caregivers most frequently discuss and seek advice for were related to caregiver stories, community support, and concerns ADRD. Specifically, we aimed to reproduce an organic Reddit search of caregiving of abuse on family member, financial struggles, symptoms of hallucinations, and repetition in ADRD family members. These results underscore the importance of online communities for gaining a comprehensive understanding of the multifaceted experiences and challenges faced by ADRD caregivers.

Characterization of the technological domain of relational citation analyses: A study of stem cell patents

This research aims to analyze the contribution of univariate and relational citation analysis methods, applied to patents, for the identification and characterization of scientific-technological domains, in documents indexed in the Derwent Innovation Index database. The adopted method was patentometrics associated with bibliometrics, using joint analysis of the relational bibliometric citation methods: co-citation and bibliographic coupling. The corpus of the study is composed of 144 patent families. Through the bibliographic coupling, 5 theme clusters and researchers with well-defined thematic domains were observed. Employing co-citation, 23 clusters were identified, characterizing the epistemic domains related to technological currents in which stem cell inventors operate. Such results allowed us to prospect the scientific-technological scenario in this theme, which can illustrate some institutions’ innovation potentials and explain who the actors at the forefront of such research are. It is proposed that applying this methodology allies to topic modeling techniques.

GTR: An explainable Graph Topic-aware Recommender for scholarly document

In the ever-expanding digital library of scholarly articles, navigating through the vast amount of available research papers to find relevant work poses a significant challenge to researchers. Addressing this issue, we introduce the Graph Topic-aware Recommender (GTR), an innovative end-to-end deep neural model tailored for scholarly recommendation systems. Unlike traditional methods that primarily rely on Collaborative Filtering, Content-Based Filtering, and Graph-Based approaches with limited consideration of the intricate citing logic within scientific documents, GTR captures the nuanced relationships and citing topics inherent in scholarly networks. By leveraging an advanced neural topic modeling technique, GTR transfers item-to-user recommendation into an item-to-item framework, facilitating a more accurate and contextually relevant paper recommendation process. Our study leverages the contextual richness of scholarly networks through Graph Neural Networks (GNNs), addressing the overlooked aspect of differentiated semantic relationships within these networks. The model stands out by effectively mining relation topics and conducting differentiated representations to minimize information redundancy, which enables GTR to adaptively infer latent citation topics, enhancing the model’s explainability and recommendation accuracy. Besides, the optimization function of GTR incorporates a novel component pooling module, designed to encode the sub-graph information of samples without traditional message passing, thereby improving the model’s efficiency and scalability. Through comprehensive experiments on multiple real-world scholarly datasets, GTR demonstrates superior performance over existing state-of-the-art models, offering both high accuracy and explainability in its recommendations.

FedExLSA

Every government in the world has multiple departments that must function and operate to address the various inquiries raised by the population. The government's diverse range of websites offers citizens a platform to submit inquiries, thereby facilitating the fulfilling of their requirements. Comprehending the subjects addressed in People Query is essential for government services. Unstructured query data is analyzed using extracting information from text techniques such as allocation of Latent diffuser (LDA) and analysis of hidden semantics (LSA). LSA outperforms other methods in terms of performance because of its minimal complexity and quick installation process. Research on decentralized learning techniques for natural language processing (NLP) is necessary due to concerns about limited data availability and privacy. Federated learning (FL) employs methods that enable different users to collectively train an integrated broad model while maintaining their information regionally stored and accessible. Nevertheless, the current body of literature lacks a thorough examination and evaluation of FL techniques. Data federation is an approach to data integration that allows the government to access and query data from multiple diverse sources as if they were a single, unified repository. Functioning as a form of data virtualization, it facilitates the creation of a comprehensive representation of data, thereby enhancing operational efficiency and the accuracy of decision-making. FedEx utilizes Federated Learning to apply topic modelling techniques to common NLP tasks. The proposed structure integrates the FL Methodology with Latent Semantic Analysis to deliver outcomes for intelligent data analysis and management.

Generative artificial intelligence in topic-sentiment classification for Arabic text: a comparative study with possible future directions

Social media platforms have become essential for disseminating news and expressing individual sentiments on various life topics. Arabic, widely used in the Middle East, presents unique challenges for sentiment analysis due to its complexity and multiple dialects. Motivated by the need to address these challenges, this article develops methods to overcome the lack of topic-based labeling techniques, compares different approaches for preparing extensive, annotated datasets, and analyzes the efficacy of machine learning (ML), deep learning (DL), and large language models (LLMs) in classifying Arabic textual data. Our research utilizes the topic-modeling technique to build a topic-based sentiment dataset of Arabic texts aimed at enhancing our understanding and processing capabilities. We present a comprehensive evaluation of dataset balancing techniques, including under-sampling, over-sampling, and using imbalanced datasets, providing insights into how these approaches impact classification outcomes. Additionally, we explore the influence of dataset sizes on the performance of various ML models, highlighting the importance of dataset scale in developing effective Arabic NLP applications. A further focus of our study is the comparative analysis of generative artificial intelligence (AI) models, including the emerging ChatGPT LLM, assessing their effectiveness in managing the complexities of Arabic language classification tasks. Our results show that support vector machines (SVM) achieved the highest performance, with F1-scores of 0.97 and 0.96 in classifying sentiment and topic, respectively, in Arabic tweets. This research not only benchmarks existing methodologies but also paves the way for more nuanced and robust models in the future, enhancing the application of generative AI in Arabic topic-based sentiment analysis.

Finance Research and the UN Sustainable Development Goals – An analysis and forward look

The relationship between academic finance research and the United Nations Sustainable Development Goals (UN SDGs) has gained increasing attention over recent years. This study conducts a comprehensive analysis of the interplay between the UN SDGs and scholarly output in financial journals from 2010 to 2022. Utilizing a framework that combines fuzzy matching, bibliometric analysis, and topic modelling techniques, the research traces the evolution of finance research and its alignment with the UN SDGs. Our findings indicate that academic finance research is unevenly spread across the SDG’s. Key areas around the natural world, sustainability and climate change are underresearched. The implications of our study are significant for both academic scholars and policymakers, suggesting a need for strategic redirection in research priorities to better support global sustainability initiatives in the future.

Semantic clustering method using integration of advanced LDA algorithm and BERT algorithm

The subject of the study is an in-depth semantic data analysis based on the modification of the Latent Dirichlet Allocation (LDA) methodology and its integration with the bidirectional encoding representation of transformers (BERT). Relevance. Latent Dirichlet Allocation (LDA) is a fundamental topic modeling technique that is widely used in a variety of text analysis applications. Although its usefulness is widely recognized, traditional LDA models often face limitations, such as a rigid distribution of topics and inadequate representation of semantic nuances inherent in natural language. The purpose and main idea of the study is to improve the adequacy and accuracy of semantic analysis by improving the basic LDA mechanism that integrates adaptive Dirichlet priorities and exploits the deep semantic capabilities of BERT embeddings. Research methods: 1) selection of textual datasets; 2) data preprocessing steps; 3) improvement of the LDA algorithm; 4) integration with BERT Embeddings; 5) comparative analysis. Research objectives: 1) theoretical substantiation of LDA modification; 2) implementation of integration with BERT; 3) evaluation of the method efficiency; 4) comparative analysis; 5) development of an architectural solution. The results of the research are that, first of all, the theoretical foundations of both the standard and modified LDA models are outlined, and their extended formula is presented in detail. Through a series of experiments on text datasets characterized by different emotional states, we emphasize the key advantages of the proposed approach. Based on a comparative analysis of such indicators as intra- and inter-cluster distances and silhouette coefficient, we prove the increased coherence, interpretability, and adaptability of the modified LDA model. An architectural solution for implementing the method is proposed. Conclusions. The empirical results indicate a significant improvement in the detection of subtle complexities and thematic structures in textual data, which is a step in the evolutionary development of thematic modeling methodologies. In addition, the results of the research not only open up the possibility of applying LDA to more complex linguistic scenarios, but also outline ways to further improve them for unsupervised text analysis.

Leveraging Narrative Feedback in Programmatic Assessment: The Potential of Automated Text Analysis to Support Coaching and Decision-Making in Programmatic Assessment.

Current assessment approaches increasingly use narratives to support learning, coaching and high-stakes decision-making. Interpretation of narratives, however, can be challenging and time-consuming, potentially resulting in suboptimal or inadequate use of assessment data. Support for learners, coaches as well as decision-makers in the use and interpretation of these narratives therefore seems essential. We explored the utility of automated text analysis techniques to support interpretation of narrative assessment data, collected across 926 clinical assessments of 80 trainees, in an International Medical Graduates' licensing program in Australia. We employed topic modelling and sentiment analysis techniques to automatically identify predominant feedback themes as well as the sentiment polarity of feedback messages. We furthermore sought to examine the associations between feedback polarity, numerical performance scores, and overall judgments about task performance. Topic modelling yielded three distinctive feedback themes: Medical Skills, Knowledge, and Communication & Professionalism. The volume of feedback varied across topics and clinical settings, but assessors used more words when providing feedback to trainees who did not meet competence standards. Although sentiment polarity and performance scores did not seem to correlate at the level of single assessments, findings showed a strong positive correlation between the average performance scores and the average algorithmically assigned sentiment polarity. This study shows that use of automated text analysis techniques can pave the way for a more efficient, structured, and meaningful learning, coaching, and assessment experience for learners, coaches and decision-makers alike. When used appropriately, these techniques may facilitate more meaningful and in-depth conversations about assessment data, by supporting stakeholders in interpretation of large amounts of feedback. Future research is vital to fully unlock the potential of automated text analysis, to support meaningful integration into educational practices.

Topic Trends Analysis in Chatbot-Related Studies Using Topic Modeling Techniques: Insights from South Korean English Pedagogy

Topic Trends Analysis in Chatbot-Related Studies Using Topic Modeling Techniques: Insights from South Korean English Pedagogy

‘엄마표 영어’ 담론 분석: 토픽모델링과 감성분석의 적용

This study was conducted to analyze the discourse on “Mother-guided English” using data from online parenting communities. To analyze the themes of the discourse on “Mother-guided English,” the topic modeling technique based on the LDA algorithm was applied, and KoGPT2 was used for sentiment analysis. As a result of the topic modeling analysis, 16 topics were derived and categorized into four main themes: “Concerns related to Mother-guided English,” “Mom's study and solidarity for Mother-guided English,” “Teaching methods and effects of Mother-guided English,” and “Educational materials for Mother-guided English”. The sentiment analysis showed a tendency toward positive sentiment overall. The application of sentiment analysis to the 16 topics revealed that positive sentiment predominated in topics such as “Concerns about Mother-guided English teaching methods,” “Mother-guided English progress,” “How to start Mother-guided English,” “Formation of Mother-guided English study groups,” and “Formation of practical English project solidarity.” On the other hand, topics such as “Concerns about choosing between Mother-guided English and other English teaching methods” and “Level tests” showed a tendency toward higher negative sentiment. The research results can expand academic discussions necessary for supporting English education for children’s parents and provide foundational data needed to develop related educational policy alternatives.

The development of music education philosophy in mainland China during 1990–2022: A corpus-based thematic review

This study used corpus-based text analysis to examine the discourse on Music Education Philosophy (MEP) in 346 Chinese scholarly articles published after 1990. Applying topic modelling techniques, the study identified 685 key terms in the corpus that collectively established six prevailing themes: Aesthetics, Praxis, Confucianism, Teaching and Learning, Values of music education, and Influence factors of music education. It was discovered that Aesthetics, Praxis, and Confucianism were three streams in Chinese literature that significantly influenced the MEP study landscape. In contrast, the other three topics served as the ‘battleground’ for these competing philosophical paradigms. The study discussed the sources of contemporary MEP in China, where combined influences from European North American Chinese philosophies are evident. It was argued that music, education, and philosophy emerge as three equally critical components that collectively define the inherent ‘nature’ of MEP. Thus, while the nature of music does not predetermine the nature of music education, the foundation of music education was shaped collectively by understandings, judgements, and expectations of music and education in the Chinese context. In addition, corpus analysis presented a promising methodological approach for tracing the evolution of discourse over time and facilitating critical inquiries in MEP discourses.

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Topic modeling has emerged as a successful approach to uncovering topics from textual data. Various topic modeling techniques have been introduced, ranging from traditional algorithms to those based on neural networks. In this research, we explore advanced topic modeling techniques, including BERT-based approaches, to enhance the analysis of scientific articles. We first investigate a widely used Latent Dirichlet Allocation (LDA) model and then explore the capabilities of BERT, to automatically uncover latent topics within scientific papers. The goal of this study is to identify the optimal hyperparameter setting for BERT-based topic modeling of scientific articles. We conduct experiments across several scenarios involving combinations of word embedding, dimension reduction, and clustering methods. The results were analyzed based on the coherence values, average execution time, number of topics generated, visualization through the inter-topic distance map, and the top-N-words of each topic. Our findings suggest that combination of RoBERTa for word embedding, PCA for dimension reduction, and K-Means for clustering yields superior results among the tested scenarios. Further evaluation of BERT-based topic modeling is necessary to validate these findings and explore its applications in various academic and industrial contexts. The implications of these advanced techniques could significantly streamline the process of staying updated with scientific literature, potentially revolutionizing research methodologies across disciplines.

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Abstract Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.

Evaluating the Performance of Topic Modeling Techniques for Bibliometric Analysis Research: An LDA-based Approach

Digital technologies have been used for a vast amount of bibliometric analysis research. Although these technologies have made scientific investigation more accessible and efficient, scholars now face the daunting task of sifting through an overwhelming number of documents. This study aims to identify bibliometric research analysis's primary topics, categories, and latent topics from a global perspective. This study utilized topic modeling techniques to analyze the abstracts of 16,039 eligible papers published between 1977 and 2023 in the Scopus database. Through the use of Latent Dirichlet Allocation (LDA) topic modeling, the study was able to identify four distinct research topics and observe how they have evolved over time. The research topic has shifted its focus from individual concepts and words to relationships between nodes and conceptual, intellectual, and social structures. The study’s findings have significant implications for bibliometric analysis-related research, providing valuable insights into trends and patterns in bibliometric analysis content within large digital article archives. The LDA has proven to be an efficient tool for analyzing these trends and patterns quickly. This study's novel approach considers factors for word embedding usage and optimal topic numbers. It focuses on a full understanding of the LDA results and combines statistical analysis, domain knowledge, and temporal exploration to better understand how data structures work. Doi: 10.28991/HIJ-2024-05-02-07 Full Text: PDF