Natural Language Processing Methods Research Articles

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.

Combining statistical dialog management and intent recognition for enhanced response selection

Abstract Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.

Sentimental analysis and prediction of socioeconomic disasters tweets by ML and regular expression

Sentiment analysis is a method of analyzing emotions and using text analysis techniques with natural language processing methods. Sentiment analysis uses data from various sources to identify the user’s attitude through different aspects. It is widely used for extracting opinions and recognizing sentiments, which helps Business organizations understand the user’s needs. This paper proposes a simple but compelling sentiment analysis method, showing the combined scores based on positive and negative words. Then, the tweets are categorized as Neutral, Negative, or Positive according to the scores. Sentiment analysis and opinion mining have grown significantly in the last decade. Different studies in this domain try to determine people’s feelings, opinions, and emotions about something or someone. The main objective of this analysis is to determine the sentiment of the review using a machine learning model and then compare the result with the manual review of the data. This would allow researchers to represent and analyze opinions objectively across different domains. A hybrid method that combines a supervised machine learning algorithm with natural language processing techniques is suggested for review analysis. This project aims to find the best model to predict the sentiment of the tweets on airlines. During the research process and considering various methods and variables that should be considered, we found that methods like naïve Bayes and random forest were not fully explored. The proposed system improves an effective and more feasible method for sentimental analysis using machine learning, multinomialNB, linear regression, and regular expression.

Abstract PO5-12-07: Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation

Abstract Understanding the early breast cancer (eBC) patient experience of disease and treatment is critical to addressing unmet patient needs, improving outcomes, and informing the next wave of drug development. Traditional approaches, such as interviews or focus group studies, are resource intensive and have limitations in reflecting the diverse experiences of patients with different disease characteristics, treatments, and sociodemographic backgrounds, and in detecting rare severe events that could potentially lead to therapy discontinuation. To overcome these limitations, we have developed a semi-automated approach utilizing Natural Language Processing (NLP) and Machine Learning (ML) methods in application to public patient forums to extract patient insights. Specifically, we analyzed 500,000 anonymized posts from the BreastCancer.org forum, utilizing Transformer-based methods to identify posts related to eBC patient experiences treated with standard hormonal therapies (HT). First, a novel ML-based model identified posts related to eBC adjuvant HT. For those, patient demographics, symptoms and their severity, and symptom management strategies such as exercise and yoga were subject to further analysis. Out of 362,074 relevant eBC posts, 32 symptoms and 9 impacts were significantly associated with at least one of the six hormonal therapies we considered. Hot flushes (relative risk [RR]: 6.70; 95% CI: 3.36–13.36), arthralgia (RR: 6.67; 95% CI: 3.53–12.59), weight increase (RR: 4.83; 95% CI: 3.20–7.28), mood swings (RR: 7.36; 95% CI: 5.75–9.42), insomnia (RR: 4.76; 95% CI: 3.14–7.22), and depression (RR: 3.05; 95% CI: 1.71–5.44) demonstrated the strongest associations. Severe headache, dizziness, back pain, and muscle spasms showed significant associations with ≥1 HT despite their low overall prevalence in eBC posts. Notably, posts contained a wide range of symptoms severity, including rarely seen symptoms in smaller patient pools, such as severe night hot flashes leading to treatment discontinuation. Symptoms/impacts mentioned in association with exercise and yoga as a way for symptom mitigation included hot flashes, arthralgia, fatigue, anxiety, depression, and pain. Of those posts, 53.3% to 82.5% reported positive or neutral sentiments for exercise, and 22.2% to 100.0% for yoga. For posts specifically mentioning the management of side effects of HT, between 50.0% and 63.5% had a positive sentiment associated with exercise concerning each of the symptoms/impacts with a lower positive impact related to yoga (between 11.1% and 43.0% of posts). Posts discussing pain management and exercise included a higher proportion of negative feedback, versus positive sentiment for depression. Posts related to yoga as an HT symptom mitigation strategy had a higher prevalence of negative sentiments for anxiety and depression. Our proposed ML/NLP-based approach to collecting patient feedback from publicly available forums complements standard qualitative methods by enabling the scalable extraction of insights from hundreds of thousands of online conversations that could span a broad diversity of symptoms and well-being reflections. Furthermore, our findings suggest the potential value of informing clinical outcome assessment (COA) measurement for clinical trials, alone and in combination with standard qualitative research approaches to guide the next clinical development. Overall, our study highlights the significant potential of NLP and ML to address the critical need to better understand the eBC patient experience of disease and treatment to improve outcomes for patients. Citation Format: Sameet Sreenivasan, Chao Fang, Emuella Flood, Natasha Markuzon, Jasmine Sze. Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-12-07.

Experiences of Women With Medical Abortion Care Reflected in Social Media (VEILLE Study): Noninterventional Retrospective Exploratory Infodemiology Study.

Abortion (also known as termination of pregnancy) is an essential element of women's reproductive health care. Feedback from women who underwent medical termination of pregnancy about their experience is crucial to help practitioners identify women's needs and develop necessary tools to improve the abortion care process. However, the collection of this feedback is quite challenging. Social media offer anonymity for women who share their abortion experience. This exploratory infodemiology study aimed to analyze, through French social media posts, personal medical symptoms and the different experiences and information dynamics associated with the medical abortion process. A retrospective study was performed by analyzing posts geolocated in France and published from January 1, 2017, to November 30, 2021. Posts were extracted from all French-language general and specialized publicly available web forums using specific keywords. Extracted messages were cleaned and pseudonymized. Automatic natural language processing methods were used to identify posts from women having experienced medical abortion. Biterm topic modeling was used to identify the main discussion themes and the Medical Dictionary for Regulatory Activities was used to identify medical terms. Encountered difficulties were explored using qualitative research methods until the saturation of concepts was reached. Analysis of 5398 identified posts (3409 users) led to the identification of 9 major topics: personal experience (n=2413 posts, 44.7%), community support (n=1058, 19.6%), pain and bleeding (n=797, 14.8%), psychological experience (n=760, 14.1%), questioned efficacy (n=410, 7.6%), social pressure (n=373, 6.9%), positive experiences (n=257, 4.8%), menstrual cycle disorders (n=107, 2%), and reported inefficacy (n=104, 1.9%). Pain, which was mentioned in 1627 (30.1%) of the 5398 posts by 1024 (30.0%) of the 3409 users, was the most frequently reported medical term. Pain was considered severe to unbearable in 24.5% of the cases (399 of the 1627 posts). Lack of information was the most frequently reported difficulty during and after the process. Our findings suggest that French women used social media to share their experiences, offer and find support, and provide and receive information regarding medical abortion. Infodemiology appears to be a useful tool to obtain women's feedback, therefore offering the opportunity to enhance care in women undergoing medical abortion.

The inverted U-shaped relationship between information entropy of keyword combinations and sales of digital products: Evidence from China Tmall

As a result of a consumer's online purchase search task, the combination of keywords plays a crucial role in decision-making and sales on e-commerce platforms. The current research aims to examine how the information entropy of keyword combinations influences digital product sales. The data (N = 4755) was collected from Tmall.com, a prominent e-commerce platform in China, and the methods of natural language processing and regression were employed to process data. Our findings demonstrate that there is an inverted U-curve between the information entropy of keyword combinations that contains product name information entropy (PNIE) and brand name information entropy (BNIE) and sales instead of a linear relationship. Furthermore, logistics evaluation (LE) and seller service quality (SSQ) play differentiated moderating effects on the inverted U-curve relationship leading to various movement of turning points and shape changes of steepness or flatness. These findings contribute to understanding sales from the viewpoint of information entropy and providing compelling guidance for e-commerce enterprises on keyword advertising bidding, logistics, and service.

A critical assessment of using ChatGPT for extracting structured data from clinical notes.

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline

Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on, and they have their own set of challenges. In this study, we propose Sentence Extractor with Keywords (SE-K), an efficient and interpretable classification approach for extracting information from clinical notes and show that it outperforms more computationally expensive methods in text classification. Following the Institutional Review Board (IRB) approval, we used SE-K and two embedding based NLP approaches (Sentence Extractor with Embeddings (SE-E) and Bidirectional Encoder Representations from Transformers (BERT)) to develop comprehensive registry of anterior cruciate ligament surgeries from 20 years of unstructured clinical data at a multi-site tertiary-care regional children's hospital. The low-resource approach (SE-K) had better performance (average AUROC of 0.94 ± 0.04) than the embedding-based approaches (SE-E: 0.93 ± 0.04 and BERT: 0.87 ± 0.09) for out of sample validation, in addition to minimum performance drop between test and out-of-sample validation. Moreover, the SE-K approach was at least six times faster (on CPU) than SE-E (on CPU) and BERT (on GPU) and provides interpretability. Our proposed approach, SE-K, can be effectively used to extract relevant variables from clinic notes to build large-scale registries, with consistently better performance compared to the more resource-intensive approaches (e.g., BERT). Such approaches can facilitate information extraction from unstructured notes for registry building, quality improvement and adverse event monitoring.

Diachronic Semantic Tracking for Chinese Words and Morphemes over Centuries

Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale historical corpus, for instance, analyzing which senses appear, increase, or decrease at which times. However, there is still a lack of Chinese diachronic corpus and dataset in this field to support supervised learning and text mining, and at the method level, few existing works analyze the Chinese semantic changes at the level of morpheme. This paper constructs a diachronic Chinese dataset for semantic tracking applications spanning 3000 years and extends the existing framework to the level of Chinese characters and morphemes, which contains four main steps of contextual sense representation, sense identification, morpheme sense mining, and diachronic semantic change representation. The experiment shows the effectiveness of our method in each step. Finally, in an interesting statistic, we discover the strong positive correlation of frequency and changing trend between monosyllabic word sense and the corresponding morpheme.

The Integration of Kansei Engineering and Kano Model on Natural Language Processing (NLP) to Support Development of Service Product in the Borobudur Temple Tourism

The Borobudur Temple Compounds is one of the spearheads of Indonesian tourism, anticipating introduction to the international community. Currently, the management of the compound is focused on maintaining the existing condition of tourism and increasing enthusiasm among visitors to experience the temple area. Amidst the challenges posed by the COVID-19 pandemic, the management has implemented stringent health procedures to ensure the safety and health of visitors. Therefore, this study aimed to investigate the service required to increase visitor enthusiasm and satisfaction in the Borobudur Temple Compounds using the Natural Language Processing (NLP) method to extract visitor sentiments as Kansei words from TripAdvisor. The results obtained were processed using Kansei Engineering type 1 to determine the most critical service quality category through Kano model. The integration of NLP, Kansei Engineering, and Kano model proved effective, showing attributes like popular Tripadvisor searches, such as exit, morning, temple, sunrise, and heat. Other attributes not listed but considered necessary based on Kansei Engineering and Kano model integration were identified, such as ticket prices, elephant enclosure management, and pamphlets. Based on the results, it was discovered that the management of Borobudur Temple Compounds needed to improve in two main aspects. These included the distance between each facility and the compound elephant cage, considered unenjoyable.

Analysis of Critical Incident Reports Using Natural Language Processing.

To demonstrate how natural language processing (NLP) methods can help in retrieving valuable information from such incident data. We analyzed frequently occurring terms and sentiments as well as topics in data from the Swiss National CIRRNET database from 2006 to 2023 using NLP and BERTopic modelling. We grouped the topics into 10 major themes out of which 6 are related to medication. Overall, they reflect the global trends in adverse events in healthcare (surgical errors, venous thromboembolism, falls). Additionally, we identified errors related to blood testing, COVID-19, handling patients with diabetes and pediatrics. 40-50% of the messages are written in a neutral tone, 30-40% in a negative tone. The analysis of CIRS messages using text analysis tools helped in getting insights into common sources of critical incidents in Swiss healthcare institutions. In future work, we want to study more closely the relations, for example between sentiment and topics.

Awareness about the Agriculture Needs through Interactive Web Application for Farmers

Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment, etc. Some smallholders Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment, etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing methods to help farmers use different farming methods, and further help them to answer their queries and solve their basic and intermediate-level doubts using a chatbot which will save their time. To meet all the requirements of farmers, a chatbot is proposed using natural language processing technology. The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterweb bot libraries and the Django framework

Awareness about the Agriculture Needs through Interactive Web Application for Farmers

Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing methods to help farmers use differentfarming methods, and further help them to answer their queries and solve their basic and intermediate level doubts using chatbot which will save their time. To meet all the requirements of farmers, a chatbot is proposed using natural language processing technology. The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterweb bot libraries and Django framework

Enhancing the Identification of False News using Machine Learning Algorithms: A Comparative Study

In today's digital world filled with information overload, preventing the rampant spread of fake news has become an urgent task. Discover the Fake News Prediction System (FNPS), which uses advanced machine learning and technology to provide innovative solutions and powerful methodologies. Natural language processing methods. FNPS uses sophisticated feature engineering from diverse, curated datasets to identify underlying patterns in fraudulent content and significantly improves the ability to recognize authenticity. FNPS achieves outstanding performance using a combination of classifiers combining TF-IDF vectorization, deep learning architecture, and sentiment analysis, demonstrating its ability to accurately predict the legitimacy of news articles. Beyond simple forecasting, FNPS provides an intuitive user interface for evaluating news content in real time. This not only increases accessibility but also promotes media literacy and responsible consumption of information. Provides additional information and promotes robust public discourse. FNPS essentially demonstrates the revolutionary potential of advanced technology in ongoing combat. This will further the important public goal of ensuring the reliability and integrity of information in the digital age.

Regional Economic Sentiment: Constructing Quantitative Estimates from the Beige Book and Testing Their Ability to Forecast Recessions

We use natural language processing methods to quantify the sentiment expressed in the Federal Reserve's anecdotal summaries of current economic conditions in the national and 12 Federal Reserve District-level economies as published eight times per year in the Beige Book since 1970. We document that both national and District-level economic sentiment tend to rise and fall with the US business cycle. But economic sentiment is extremely heterogeneous across Districts, and we find that national economic sentiment is not always the simple aggregation of District-level sentiment. We show that the heterogeneity in District-level economic sentiment can be used, over and above the information contained in national economic sentiment, to better forecast US recessions.

Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data.

Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related. We accessed and digitized court records from the state of Washington (n = 1472). Human review established a gold standard label for firearm involvement (yes/no). We developed a key term search and trained supervised machine learning classifiers for this labeling task. Results were evaluated in a held-out test set. The decision tree performed best (F1 score: 0.82). The key term list had perfect recall (1.0) and a modest F1 score (0.65). This case report highlights the accuracy, feasibility, and potential time-saved by using NLP to identify firearm involvement in alleged violent crimes based on digitized narratives from court documents.

Sentiment Analysis on The Place of Interest in Malaysia

This study focuses on utilizing machine learning methods for sentiment analysis to identify positive and negative comments regarding Malaysian Places of Interest (PoI). The data was collected from Twitter using social media monitoring software and organized into tables. Pre-processing techniques and Natural Language Processing (NLP) methods were applied to handle missing values and prepare the text data for analysis. The dataset was then split into training and testing sets, and three supervised learning algorithms which are Support Vector Machine, Random Forest, and Naive Bayes were employed to evaluate the sentiment analysis models. The performance of each model was compared, and it was found that Support Vector Machine achieved the highest accuracy, recall score, F1 score, and precision score. This study demonstrates the potential to extend sentiment analysis to analyze sentiments expressed in texts written in the Malay language by utilizing the Malaya corpus. Additionally, visual dashboards can be created to present the findings and provide recommendations based on the insights gathered from the sentiment analysis of PoI feedback.

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

Exploring the connotation of “Local Community” in ICOMOS discourse: a textual analysis based on authoritative documents

As an authoritative institution in cultural heritage conservation, ICOMOS plays a crucial role in guiding local communities’ participation in heritage conservation. However, its scattered and vague descriptions of local communities pose significant obstacles to further research and practice of community participation in heritage conservation. Given the increasing importance of local communities in heritage conservation, it is essential to systematically explore the connotation of Local Community connotations within ICOMOS discourse. This research employs Natural Language Processing methods to analyze ICOMOS’s descriptions of Local Community. Utilizing computational techniques of word frequency calculation, LDA (Latent Dirichlet Allocation) topic model keyword calculation, and hierarchical clustering calculation, the research uncovers relevant keywords and its thematic clusters of Local Community. These findings are further elucidated by aligning them with the principles outlined in authoritative documents of ICOMOS. The research indicates that ICOMOS’s descriptions of Local Community can be summarized into four main keywords families. These keywords families can be summarized as a comprehensive Local Community “three-level, four-family” keywords system. The “Tourism” keywords family exhibits a close association with Local Community, highlighting ICOMOS’s heightened emphasis on heritage tourism. The “Management-Development” keywords family occupies the second level, emphasizing fundamental principles for local communities’ participation in heritage practices. The “Traditional-Knowledge” and “Social-Economic” Keywords Families, situated in the third level, respectively describe the value attributes and conservation approaches of local communities. Each keywords family formed over different periods, exhibiting varying trends of development. By systematically integrating ICOMOS’s descriptions of Local Community and employing Natural Language Processing for in-depth exploration, This research aims to construct a cognitive understanding of local communities from a new perspective of quantitative text analysis, with the intention of providing theoretical references for subsequent research on local communities.

Education hubs in Qatar and the United Arab Emirates: Analysis of discursive constructions using Latent Dirichlet Allocation

Advanced techniques for linguistic analysis provide the opportunity to extract meanings from the available documents on education hubs. We use Latent Dirichlet Allocation (LDA), a topic modelling method for natural language processing and big data mining, to analyze the discourse of education hubs in Qatar and the United Arab Emirates (UAE). Our data derive from a corpus of academic literature, non-peer-reviewed articles, and policy documents. The analysis shows that education hubs have been at the core of efforts to transition from a hydrocarbon-based economy to a knowledge economy. The analysis also shows that education hubs have been increasingly associated with the two countries’ efforts to achieve regional leadership in overlapping geopolitical spheres of influence. Describing the situation as a ‘constructive rivalry’, we highlight its geopolitical implications and we call attention to Knight, (2022) knowledge diplomacy whereby the two countries can use education hubs as a mechanism for exercising leadership in promoting peace and development in the broader region of the Middle East.c.