Latent Dirichlet Allocation Algorithm Research Articles

Examination of experience attributes of parks in urban tourist destinations and their influence on visitor satisfaction: a topic modelling approach

ABSTRACT Understanding visitors’ park experience in urban tourist destinations to enhance their satisfaction is crucial for effective park management and urban planning. Through a topic modelling analysis using user-generated data from TripAdvisor and employing the Latent Dirichlet Allocation (LDA) algorithm, 20 attributes of urban park experience are identified and grouped into three categories and five dimensions. Besides confirming some of the attributes of park experience identified in previous research, this study also uncovers new attributes that provide insights into visitors’ urban park experience related to the historical and cultural aspects (e.g. filming location, carriage rides and rickshaws). Furthermore, positive and negative effects of each attribute on visitor satisfaction are identified. Findings also suggest significant seasonal difference in attributes of visitors’ urban park experience.

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.

An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

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.

A topic modeling-based analysis for the outcomes of psychological contract breaches and violations in organizations: Current research trends and future agenda

The purpose of this study is to examine the temporal trends of conceptualizations of psychological contract breaches and violations in organizations and their outcomes. Thus, it is envisaged to provide a deep insight into the related topic and to contribute to the elucidation of unexplored aspects by guiding a comprehensive understanding of the perceptual underlying phenomenon. In this study, the topic-modeling method, one of the text-mining methods, using the Latent Dirichlet Allocation (LDA) algorithm was used to gain insights into the main topics on which studies on psychological contract breaches and violations were conducted. Within the framework of the purpose, this study reveals the topics belonging to the concept of psychological contract breaches and violations in organizations. In addition, the titles of these topics, the changes in the interest in these topics over the years, and especially during the COVID-19 outbreak, have been revealed. The ’findings of the study indicate that future research should focus on the affective outcomes of psychological contract breaches and violations in organizations. In addition, future studies can be conducted in which the topics that have reached research saturation in the current study are addressed together with the topics that need research attention.

Modeling Climate Change Issues in Indonesia Based on Media Headlines

Climate change has become a global issue affecting all countries in the last decades. This phenomenon poses a concern to Indonesia as it is one of the climate change’s epicenters. Various studies have shown that climate change can harm multiple community activities, such as unstable agricultural production, decreased people’s health, and global warming. This study tried to model and analyze climate change topics discussed in the media. Finding hidden topics from texts can provide clues and information regarding public conversation surrounding climate change, such as public thoughts, perceptions, and readiness to mitigate the possible adverse effects of climate change. In order to identify hidden subjects from the corpus, this work modeled climate change issues in Indonesia using the latent Dirichlet allocation (LDA) algorithm to analyze texts from Indonesian media headlines. As many as 7,000 headline data from five online media were collected from 2017 to 2021 using web scraping techniques. The proposed approach produced eight topics related to climate change, which were determined by the highest coherence value of 0.560. Those topics were renewable energy, carbon emissions, environmental management, development economics, international cooperation, policy/regulation, rehabilitation, and disaster. Based on the results, the model could sufficiently describe the theme of discussion in society and photograph public thoughts and the government’s readiness in the form of policies and regulations in dealing with the climate change phenomenon.

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling.

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system.

Value‐centric analysis of user adoption for sustainable urban micro‐mobility transportation through shared e‐scooter services

AbstractMicro‐mobility services, which are considered a sustainable alternative to traditional transportation modes, have gained substantial popularity due to advancements in mobile technology. As one of those modes of transportation, shared e‐scooter services have encouraged several startups in urban areas, allowing them to reach massive numbers of consumers in a highly competitive environment. This study aims to explore gains and barriers that affect the intention of consumers to use shared e‐scooter services, all within the framework of sustainability‐driven considerations. The Latent Dirichlet Allocation (LDA) Algorithm was used to analyse 24.798 reviews from the Google Play Store, uncovering eight topics. Those topics were used to discover customer value perceptions in the shared e‐scooter context and compare them with the related literature on perceived value. Besides, their impact on user ratings on the mobile application platform was measured using machine learning algorithms. The study's findings are expected to contribute to developing regulations for shared e‐scooter services, which have gained popularity as an eco‐friendly mode of transportation sustainability in urban areas by introducing a novel perspective.

Pre-Training Clustering Models to Summarize Vietnamese Texts

Our investigation aims at pre-training clustering models to summarize Vietnamese texts. For this purpose, we create a large-scale dataset by collecting Vietnamese articles from newspaper websites and extracting the plain text to build the dataset, including 1,101,101 documents. We propose a new single-document extractive text summarization model based on clustering models. Our proposal clusters the documents with the hard clustering k-means algorithm and the soft clustering LDA (Latent Dirichlet Allocation) algorithm. Then, based on the pre-training clustering models, a summary model is used to select the salient sentence in the input text to construct the summary. The empirical results showed that our summary model achieved 51.22% ROUGE-1, 17.62% ROUGE-2 and 29.16% ROUGE-L on the testing set. Besides the traditional word representation such as BoW (Bag-of-Words), we also use the word meaning-based tools like FastText and BERT (Bidirectional Encoder Representations from Transformers) in our model. The additional benefit of our proposed extractive summary model is that the output summary is a long-text, readable document. Furthermore, the model’s architecture is straightforward, easy to understand and runs on cost-efficient resources like arm CPU and GPU too.

Literature data mining on the current research status of uveitis in China

Objective: To analyze the current research status of uveitis in China. Methods: It was a bibliometric analysis study. Using search formulas covering uveitis and its multiple subtypes, uveitis-related literature in English with publication dates from 2013 to 2022 was retrieved in Web of Science core databases through certain search strategies. This study used the latent Dirichlet allocation (LDA) algorithm to build topic models and analyzed the trends of research topics in recent years. Bibliometric analysis was used to analyze and visualize the bibliometric indicators (e.g., number of publications, citations, and H-index) of the included literature using tools such as VOSviewer software. Results: Over the past decade, China has published 1 657 papers on uveitis, ranking second globally. However, there is still room for improvement in terms of the H-index (58) and citation (12.28 per publication). Countries such as the USA (43.04%) and the United Kingdom (62.54%) were engaged in more international collaboration. We identified ten optimal LDA topics for uveitis literature in China such as immunotherapy, Behçet's disease, and Vogt-Koyanagi-Harada syndrome. Research on uveitis in China was mostly published in Ocular Immunology and Inflammation (92). Conclusions: China has made remarkable progress in uveitis research. Nonetheless, there is still untapped potential to enhance our global academic influence. It is encouraged to promote international collaborations, harness our expertise in areas like Behçet's disease and VKH syndrome, and publish our scientific achievements in high-impact journals.

Understanding tourism consumer behavior using biometric technologies: bibliographic review and research agenda

Recent technological advances, particularly in biometrics, have significantly impacted the tourism industry. Amidst the COVID-19 pandemic, these technologies have grown rapidly, posing challenges and opportunities in utilizing the resulting data. This study aims to develop a research agenda concerning biometrics in tourism consumer behavior, detailing what biometric data entails and outlining its diverse applications. Through a bibliographic review of 422 recent papers, employing machine learning and artificial intelligence techniques, we extracted keywords, topics, and frequencies using the methodological approach of corpus linguistics and latent Dirichlet allocation algorithm. The results identified 26 topics, including “KPIs”,” Techniques”, “Personalization”, “Health”, and “Travel and transport”. Furthermore, we observed that the COVID-19 pandemic has dramatically impacted the tourism sector, with “Health” present in four categories. The practical implications of our study suggest that companies can find the issues that most concern the tourism consumer in the lines of research presented in the research agenda, paying particular attention to the lines developed in the research agenda involving “personalization”, "travel and transport" and "health".

Analysis of Suicide-related Tweets During the COVID-19 Pandemic

Background:: The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. Objective:: It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Methods:: Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Results:: Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. Conclusion:: We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.

Exploring Latent Dirichlet Allocation (LDA) in Topic Modeling: Theory, Applications, and Future Directions

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

Automatic label curation from large-scale text corpus

Abstract The topic modeling technique extracts themes based on their probabilistic measurements from any large-scale text collection. Even though topic modeling pulls out the most important phrases that describe latent themes in text collections, a suitable label has yet to be found. Interpreting the topics extracted from a text corpus and identifying a suitable label automatically reduces the cognitive load for the analyst. Extractive methods are used typically to select a label from a given candidate set, based on probability metrics for each candidate set. Some of the existing approaches use phrases, words, and images to generate labels using frequency counts of different words in the text. The paper proposes a method to generate labels automatically to represent each topic based on a labeling strategy to filter candidate labels and then apply sequence-to-sequence labelers. The objective of the method is to get a meaningful label for the result of the Latent Dirichlet Allocation algorithm. The BERTScore metric is used to evaluate the effectiveness of the proposed method. The proposed method generates good interpretative labels as compared to baseline models for topic words or terms automatically. The comparison with the label generated through ChatGPT API shows the quality of the generated label with the experiment performed on Four Datasets NIPS, Kindle, PUBMED, and CORD-19.

Machine Learning Approach to Identify Case-Control Studies on ApoE Gene Mutations Linked to Alzheimer’s Disease in Italy

Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in the identification and screening of papers present in the literature that focus on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer’s Disease patients. Results: MySLR excludes duplicates and creates topics. MySLR was applied to analyze a set of 164 scientific publications. After duplicate removal, the results allowed us to identify 92 papers divided into two relevant topics characterizing the investigated research area. Topic 1 contains 70 papers, and topic 2 contains the remaining 22. Despite the current limitations, the available evidence suggests that articles containing studies on Italian Alzheimer’s Disease (AD) patients were 65.22% (n = 60). Furthermore, the presence of papers about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, the primary genetic risk factor of AD, for the Italian population was 5.4% (n = 5). Conclusion: The results show that the machine learning platform helped to identify case-control studies on ApoE gene mutations, including SNPs, but not only conducted in Italy.

A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users’ willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation.

How Reddit Sentiments Influenced Gamestop Stocks

This paper focuses on the language usage in the GameStop (GME) share rally in January 2021. Specifically, I analyze how comments in a leading post of Reddits r/wallstreetbets subreddit reflect the reddit user communitys sentiment and emotion with various linguistic expressions. I analyzed the topics of the comments using topic modeling with Latent Dirichlet allocation algorithm, which objectively identified representative topics in the comments. Results showed that there were six clusters of topics, including two of the topic clusters that represented words that were highly correlated with stock market behaviors, such as buy and sell, one cluster that suggested objective analyses of the stock market, and one cluster that presented complaining emotions. The findings indicate a direct correlation and impact between the sentiments of comments and the performance of the stock market.

Optimizing Text Clustering Efficiency through Flexible Latent Dirichlet Allocation Method: Exploring the Impact of Data Features and Threshold Modification

A parallel corpus comprising Croatian EU legislative documents automatically translated into English spans 28 years and is enriched with metadata, including creation year and hierarchical classifier tags denoting descriptors, document types, and fields. However, nearly two-thirds of the approximately 1.5 thousand texts lack complete metadata, necessitating labor intensive manual efforts that pose challenges for human administration. This incompleteness issue can be observed in the case of official legal sites functioning as regular service provisioning databases. In response, this paper introduces an artificial cognitive and multilabel classification approach to expedite the tagging process with only a fraction of the manual effort. Leveraging the Latent Dirichlet Allocation (LDA) algorithm, our method assigns field values or tags to incompletely labeled documents. We implement a Flexible LDA variant, incorporating the influence of topics close to the most probable topic, regulated by a relative probability threshold (RPT). We evaluate the LDA prediction's dependence on document prefiltering and RPT values. Furthermore, we investigate the dependence of quantitative linguistic properties on the type and speciality of pre-processing tasks. Our algorithm, built on error-correcting optimizing codes, succesfully predicts a mixture of topic probabilities for these legal texts. This prediction is achieved by calculating the Hamming distance of binary feature vectors created using the legal fields of the EUROVOC multilingual thesaurus.

(RE)VISÃO DAS CAPACIDADES DINÂMICAS: ORIGENS E DESDOBRAMENTOS FUTUROS

ABSTRACT This paper aims to map out how the field of dynamic capabilities has developed since the seminal studies by Teece et al. (1997) and Eisenhardt and Martin (2000). We identified 10,838 papers and used the Latent Dirichlet Allocation algorithm for topic modeling. We conducted two analyses: the first was based on the temporality and characteristics of the studies; the second was interpretative and based on the network of theoretical concepts. Results indicate an approximation between the ideas of the two seminal studies, which were initially viewed as opposite. We observe a movement to value relational issues, following a collective construction path, and paying less attention to the firm itself. Overall, we were able to understand the consolidation of the dynamic capabilities field; understand the core elements involved in the development of dynamic capabilities; set out the original and current concepts of dynamic capabilities; and indicate tendencies and a possible future research agenda.

Evaluating LDA and LSA for Topic Modeling in the Indonesian Natural Disaster

Topic Modeling is a method for analyzing topics, documents, and articles in Natural Language Processing. The LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) algorithms are widely used in topic modelling. This study focuses on analyzing articles related to natural disasters in the Indonesian language. The dataset for this study was obtained through data scraping from Google News, which served as a container for several articles and online news sources. The research method is divided into several stages: dataset scraping, data preprocessing, topic modelling with LDA and LSA, model visualization, and model evaluation. The results of the research show that both algorithms can generate topics on datasets relevant to natural disasters in Indonesia such as floods, earthquakes, landslides, tsunamis and so on. From the evaluation results using Coherence Scores, it is found that LDA has better performance than LSA seen from Coherence Scores in modeling topics related to Natural Disasters in Indonesia. The results of this research are expected to provide convenience for information system developers in terms of monitoring natural disasters in Indonesia and make it easier for researchers who want to effectively read a document based on the disaster related in the document.