Topic Modeling Analysis Research Articles

Topic Modeling Analysis of Chinese Medicine Literature on Gastroesophageal Reflux Disease: Insights into Potential Treatment

Topic Modeling Analysis of Chinese Medicine Literature on Gastroesophageal Reflux Disease: Insights into Potential Treatment

Exploring climate change discourse on social media and blogs using a topic modeling analysis

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

Analyzing research trends after the international journal transition of the Korean Journal of Medical Education: using topic modeling techniques.

This study aimed to analyze the research trends of the Korean Journal of Medical Education (KJME) since it became an English-language journal. A total of 274 articles published in KJME from 2016 to 2023 were analyzed. All article types were included in the analysis. NetMiner ver. 4.0 (Cyram Inc., Korea) was used for the main keyword and topic modeling analysis. Of the 274 articles, 170 (62%) and 104 (38%) were by domestic and international authors, respectively. The main keywords with high frequency were "students," "learning," "experience," "pandemic," and "perception." Three topics were derived using topic matching analysis: "residents' perception and attitude of the pandemic," "assessment of learning and achievement," and "learning experiences in the pandemic." Since the shift to English-language journals, medical education research has witnessed an increase in the number of articles published by international authors. Research on postgraduate education has increased. Research topics are relevant to situations such as the coronavirus disease 2019 pandemic. These findings can help researchers select research topics and encourage them to submit their research to the journal.

Hidden advertisements included in cancer information posts: Insights from social listening.

e21017 Background: Access to cancer information on the Internet has proliferated, yet ensuring the accuracy and reliability of such information remains a challenge. The abundance of online content necessitates a comprehensive understanding of the prevailing landscape of cancer information dissemination. Methods: Cancer-related information was retrieved from search engine results pages of NAVER and Google, the most frequently used websites for information search in South Korea, during September and October 2023. Promoted posts and overt advertisements were excluded from the dataset. Posts featuring more than 50% of advertising content were categorized as fully hidden advertisements (HA), while those with lesser proportions were classified as partially HA. In cases where there was no advertising content but the hospital name or contact information was indicated for promotional purposes, it was classified as a simple HA. Topic modeling techniques were employed to analyze the content of HA posts, identifying prevalent themes and keywords indicative of promotional intent. Results: Of the collected posts, 48.6% were classified as HA. The publisher of the HA was 26.8% of oriental medicine/care hospitals, followed by general hospitals (25.3%), non-professional (21.3%), and small and medium-sized private hospitals (17.4%). In HA, 40.3% were full advertisements, 36.75% were partial advertisements, and the remainder were simple displays. By cancer type, 65.3% of posts about breast cancer were advertising posts, 55.2% were about colon cancer, and 53.7% were about stomach cancer. In the topic modeling analysis of HA, despite containing basic and theoretical information about cancer, HA subtly directs users towards advertising content, featuring terms related to private insurance and complementary medicine. Conclusions: The study elucidates the prevalence and characteristics of HA within cancer-related information available online, highlighting the need for heightened awareness. While it may not be inherently wrong, HA can impact user perception and decision-making processes. Additional research is underway to determine whether information classified as HA can be trusted. Addressing issues such as HA within cancer information will likely require cooperation between search engine platforms and government agencies.

Research Trends on Living Donors for Liver Transplantation: A Text Network Analysis and Topic Modeling

Purpose: This study aimed to identify research topics and trends on living liver donors over time through text network analysis and topic modeling.Methods: Five electronic databases (PubMed, CINAHL, Embase, Web of Science, and PsycINFO) were reviewed for studies published through September 2023, and 392 studies were included. Text network analysis was used to identify the basic characteristics and centrality of the network. The topics were named after extracting meaningful topics through topic modeling using latent Dirichlet allocation.Results: A total of 1,111 keywords were extracted from the abstracts of 392 selected studies, among which “length of stay,” “morbidity,” “mortality,” “pain,” and “quality of life” showed high frequency and centrality. Through topic modeling analysis, the following four topics were derived: objective health indicators (topic 1), subjective health indicators (topic 2), hepatobiliary-related indicators (topic 3), and early health indicators (topic 4). An analysis of trends in these topics over time showed that the proportion of topics 1, 3, and 4 increased or remained stable. In contrast, there was no significant change in topic 2, representing subjective health indicators.Conclusion: This study explored research trends on living liver donors using text network analysis and topic modeling. Based on the main topics derived, research on postoperative outcomes for living liver donors has focused on objective health indicators, hepatobiliary-related indicators, and early health indicators compared to subjective health indicators. We suggest that future studies utilize integrated indicators of physical and psychosocial aspects.

A Comparative Study of Religious Scriptures Using Natural Language Processing

Over the past few years, Natural Language Processing (“NLP”) has emerged as a powerful tool and has enabled computational analysis of texts by offering insights into the subtleties of language, emotion, and thematic frameworks. This research paper employs NLP strategies such as topic modelling and sentiment analysis to compare translations of three religious scriptures: the Bhagavad Gita representing Hinduism, Quran representing Islam and the Bible representing Christianity. Before carrying out the tests, text was pre-processed and cleaned to ensure that the most optimum results were obtained. Topic modelling uses algorithms such as Latent Dirichlet Allocation to find prominent themes while sentiment analysis makes use of an NLTK VADER sentiment module ‘Sentiment Analyzer’ to interpret emotional undertones in text. This research paper finds that the texts share similar views on topics such as generosity, devotion to God among others and have differing opinions on themes including sacrifice and violence. It is also interesting to note that while the religions of the Bhagavad Gita and Quran (Hinduism and Islam respectively) have been pitted against each other for centuries in countries such as India, they share several similar principles and ideologies.

A computational approach to cryptocurrency marketing on social media

This study aims to explore social media content associated with cryptocurrency marketing. We employ unsupervised Latent Dirichlet allocation topic modeling and sentiment analysis techniques to 98,716 tweets to examine Twitter (now known as X) content for subjects and sentiments related to cryptocurrency. Our findings reveal that cryptocurrency tweets fell into four categories, with ‘cryptocurrency trading,’ ‘NFT airdrop,’ ‘cryptocurrency affiliate program,’ and ‘Dogecoin on social media’ being the most popular. Furthermore, most of these topics exhibited positive sentiments. This study contributes theoretically by integrating cryptocurrency marketing into the diffusion of innovation paradigm. In addition, it offers strategic insights for digital marketers in identifying prevalent topics and sentiments related to cryptocurrency, enabling the tailoring of affiliate marketing communication strategies on social media.

Cross-cultural study of hotel practices as perceived by Chinese and North American travellers: A data mining analysis

This paper aims to unearth the different perception styles of Chinese and North American travellers from analytic versus holistic thinking perspectives. Python was utilized to gather online textual data from Chinese and North American travellers, while word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and content analysis were employed to elucidate the perception styles in a cross-cultural context. In general, North American travellers mainly leaned towards analytic thinking, whereas Chinese travellers showcased a blend of holistic and analytic thought processes. The topic of travel, leisure and accommodation showed both holistic and analytic thinking styles. The topics of nature and environment, front desk service, and travel routes and scenic spot areas mainly represented a holistic thinking style. The topics of convenience and facilities, breakfast, transportation, hotel theme and features, and decoration and amenities mainly suggested an analytic thinking style. Hotels should consider the different perception styles of Chinese and North American travellers to facilitate strategies accordingly and to maximize the experience of travellers from different cultural backgrounds.

A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry

The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals.

Natural Language Processing (NLP) in Recommendation Systems

This paper delves into the application of Natural Language Processing (NLP) techniques in recommendation systems, specifically focusing on novel approaches to enhance recommendation accuracy and user satisfaction. The utilization of NLP algorithms has revolutionized how content is recommended to users, leveraging linguistic analysis and machine learning to understand user preferences and provide tailored suggestions. Our research explores various NLP methodologies, including sentiment analysis, topic modelling, and semantic analysis, to extract meaningful insights from textual data. Furthermore, we investigate the integration of deep learning models, such as neural networks and transformer architectures, to capture complex patterns and improve recommendation precision. A key highlight of our study is the introduction of a novel recommendation method termed "Knowledge Graph Embedding for Contextual Recommendation." This innovative approach combines knowledge graph representation with contextual understanding, allowing for more nuanced and personalized recommendations based on user interactions, historical data, and contextual relevance. We delve into the intricacies of this technique, detailing its implementation, training process, and evaluation metrics.

Gendered Judicial Opinions

In this paper we investigate whether gender is associated with the content of judicial opinions in the U.S. courts of appeals. Using a topic model analysis, we find that gender is a significant predictor of the content of judicial opinions. Two causal pathways could explain this result: (1) men and women judges write differently about the same cases; (2) men and women judges write about different cases, either due to assignment or selection effects. To untangle these two pathways, we carry out three additional analyses. First, we examine whether the United States as a party is associated with judge gender. We next examine whether case codes are associated with judge gender. Finally, we examine the relationship between topic prevalence and gender, controlling for case codes. Our findings lend greater support to the second pathway than the first. This result raises the prospect that prior work on gender-based differences in judicial behavior may be confounded by assignment or selection effects. Our results also raise normative concerns about gender disparities in voice and influence in the U.S. courts.

Refugee women in the media – prevalence, representation and framing in international media coverage

ABSTRACT Are refugee women invisible in the media? Extant media portrayals often make use of stereotypical representations, in that women are represented as vulnerable and passive victims. Within the theoretical frameworks of agenda setting and framing, vulnerability and agency, digital emotion contagion and distant suffering, this study investigates media coverage on refugee women by four popular international news outlets on the basis of their YouTube presence from 2011 to 2021. We applied automated data gathering to extract relevant videos which were manually coded and analyzed. In addition, we applied an automated topic modeling and sentiment analysis. The results are ambiguous but in general reflect existing findings: refugee women are marginalized in media coverage, and their portrayal is often stereotypical presenting them as vulnerable victims. The results highlight the need to sensitize journalists for a non-stereotypical reporting and the broader public for a more critical stance towards media coverage of refugee women.

Utilising a ‘Community of Practice’ to support pharmacists to work in residential aged care: protocol for a longitudinal evaluation

IntroductionA Community of Practice is briefly defined as a group of people with a shared interest in a given area of practice who work collaboratively to grow collective knowledge. Communities...

Analysis of Peatland Research Trends Based on BERTopic

Peatlands comprise approximately 3% of the land area worldwide. Peatland exists in most countries, including tropical, subtropical, and boreal regions. Accordingly, peatland has garnered increased research attention as a potential countermeasure against climate change. Therefore, it is necessary to identify and comprise the topics constituting global peatland research. In this study, we applied BERTopic—a topic modeling technique—to analyze relatedness between research topics to classify global peatland research trends, evaluate changes over time, and analyze the relationships between topics. To this end, we searched the keyword “peatland” on ScienceDirect—a global academic publication data platform—and collected the titles and abstracts from 10,158 publications from 1953 to 2022 for dynamic topic modeling and network analysis. Eighty-two peatland research topics were identified, which were combined into 15 main topics. Over time, an increasing trend was observed in topics related to production, management, and fire. In addition, upon analysis of the relationships between topics, three groups centered on fire, peatland value, and carbon were identified. We anticipate that the findings of this study can be expanded to analyze trends in research related to fires in peatlands, regional characteristics of peat soil, prediction of greenhouse gases emission and mitigation due to peatland fires, and prediction of future peatland research topics.

Dementia Knowledge in Chinese Newspapers (2005-2020): A Topic Modeling Analysis

ABSTRACT How public policies convey dementia is an important source of the public’s understanding of dementia, and newspapers are critical to depicting and disseminating this information to the public. The present study used topic modeling strategies to analyze Chinese newspaper portrayals of dementia from 2005 to 2020 to trace changes in key areas of dementia knowledge in relevant policies. Using WiseNews, the largest Chinese media database, we chose 45 newspapers from mainland China and identified 12,719 articles related to dementia. Using latent Dirichlet allocation (LDA), we performed a topic modeling analysis and identified the six most prevalent topics on dementia across articles: lifestyle recommendations, neighborhood life, foundational scientific research, celebrity and media portrayals, dementia caregiving, and pharmaceutical innovations – all related to the dementia knowledge scale’s four dimensions. Findings suggest a steady increase in the number of articles on dementia caregiving and a decline in lifestyle recommendations from 2005 to 2020. However, newspapers continued to stigmatize aging by regularly co-depicting dementia and old age and by using biased terminology. Among the first to investigate dementia’s portrayals in mainland Chinese newspapers, this study illuminates the need for expanding mass media campaigns to raise the country’s dementia knowledge to foster a dementia-inclusive society.

Media narratives on (Ecological) sustainability: A topic modeling analysis of entrepreneurship news in the MENA region

Amidst the increasing ecological pressures arising from industrial activity and consumerism, with particularly tangible impacts outside of the global north, the private sector is being held accountable to reduce its environmental footprint and transition to sustainable business models and processes. Nevertheless, perceptions of sustainability and its dimensions largely vary between cultures and contexts, while local media discourses both shape and reflect those concepts and narratives. This in turn shapes sustainability practices and the nature and impacts of emerging entrepreneurial business activity. Given the dearth of sustainability research in the Middle East & North Africa (MENA) and the region’s extreme vulnerability to climate change, pollution, draughts combined with political instability and institutional fragility, this paper provides an in-depth analysis of media narratives at the intersection of sustainability and entrepreneurship in the region. This was done by means of the machine learning method of topic modelling on two datasets of 486 and 217 news media articles extracted from a major regional English-language news outlet, followed by a manual content analysis of the extracted topics and articles. We find that the term sustainability is highly associated with economic prosperity and that ecology-related themes are often formulated in terms of financial gain, for instance transitioning out of oil & gas dependence. We find a surprising lack of focus on water scarcity and biodiversity, coupled with a welcomed focus on the energy transition, recycling, urban mobility, as well as social networking and minority integration. Our findings hence provide a nuanced contextual understanding of sustainability, which may support businesses and decision-makers alike in (re)defining priorities and reflecting upon the status quo and necessary next steps in the transition to more sustainable societies.

Public Opinions on ChatGPT : An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis

ABSTRACT The sudden arrival of AI (Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

Understanding public sentiments and misbeliefs about Sustainable Development Goals: a sentiment and topic modeling analysis

PurposeMisinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.Design/methodology/approachThe authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.FindingsThe authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.Practical implicationsThe authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.Originality/valueThis study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Public perception of psychiatry, psychology and mental health professionals: a 15-year analysis.

X (previously known as "Twitter") serves as a platform for open discussions on mental health, providing an avenue for scrutinizing public perspectives regarding psychiatry, psychology and their associated professionals. To analyze the conversations happening on X about psychiatrists, psychologists, and their respective disciplines to understand how the public perception of these professionals and specialties has evolved over the last 15 years. We collected and analyzed all tweets posted in English or Spanish between 2007 and 2023 referring to psychiatry, psychology, neurology, mental health, psychiatrist, psychologist, or neurologist using advance topic modelling and sentiment analysis. A total of 403,767 tweets were analyzed, 155,217 (38%) were in English and 248,550 (62%) in Spanish. Tweets about mental health and mental health professionals and disciplines showed a consistent volume between 2011 and 2016, followed by a gradual increase from 2016 through 2022. The proportion of tweets discussing mental health doubled from 2016 to 2022, increasing from 20% to 67% in Spanish and from 15% to 45% in English. Several differences were observed on the volume of tweets overtime depending on the language they were written. Users associated each term with varied topics, such as seeking for help and recommendation for therapy, self-help resources, medication and side effects, suicide prevention, mental health in times of crisis, among others. The number of tweets mentioning these topics increased by 5-10% from 2016 to 2022, indicating a growing interest among the population. Emotional analysis showed most of the topics were associated with fear and anger. The increasing trend in discussions about mental health and the related professionals and disciplines over time may signify an elevated collective awareness of mental health. Gaining insights into the topics around these matters and user's corresponding emotions towards them presents an opportunity to combat the stigma surrounding mental health more effectively.

Standing tall like Caesar? Qatar’s unwavering voice for Palestine at the United Nations

This article examines the complexities of Qatar’s Palestine policy, emphasising Doha’s multifaceted role as a mediator, problem-solver, humanitarian aid provider, and representative of the Palestinian issue at the United Nations. The study employs a mixed-methods research approach. Firstly, it utilises qualitative data from official statements, key policymakers’ remarks, and literature on Qatar’s foreign policy and Palestinian engagement. Secondly, it introduces text analytic methods such as latent Dirichlet allocation (LDA) topic modelling, word clouds and network analysis to identify key themes, relationships and trends in Qatar’s speeches. The findings reveal that the Palestinian issue has been a prominent and consistent topic in Qatar’s United Nations General Assembly General Debate addresses over the past five decades. Qatar has consistently advocated for a resolution to the conflict, emphasising the importance of peace, justice and the two-state solution. Qatar’s support for Palestine remains steadfast, despite pressure from other regional actors to end its engagement with Hamas and divert its attention from the Palestinian issue. This commitment to the Palestinian cause aligns with Qatar’s broader state preferences for an independent foreign policy, its opposition to external interference, and its aspiration to restore its role as a trusted international partner on global platforms.