Analyzing the research trends of COVID-19 using topic modeling approach

PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.

Impact analysis of COVID-19 pandemic on students' online exams based on topic model

To deal with the COVID-19 pandemic, schools at all levels insist on "classes suspended but learning continues"and actively implement online teaching. Different from the planned shift from offline to online education, COVID-19 caused online teaching to be highly sudden and emergent, producing different learning outcomes from offline teaching. Therefore, it is critical to analyze the epidemic's impact on students' learning outcomes. However, prior studies only focus on statistical data of the learning process, such as students' test scores or homework completion, rather than comments posted on social media. This paper explores the impact of COVID-19 on students' online exams by identifying potential topics during the final exam period. We first collect and preprocess a huge number of Weibo posts with natural language processing methods. Then, we explore related topics via LDA (Latent Dirichlet Allocation) model. Finally, the extensive experimental results demonstrate that our findings for the 16 topic groups have significant roles in exploring students' attitudes towards online exams and exam cheating. Furthermore, we found that the overall affective attitudes of users' postings tended to be negative. © 2022 IEEE.

Understanding Airline Passengers during Covid-19 Outbreak to Improve Service Quality: Topic Modeling Approach to Complaints with Latent Dirichlet Allocation Algorithm.

The COVID-19 pandemic has deeply affected the airline industry, as it has many sectors, and has created tremendous financial pressure on companies. Flight bans, new regulations, and restrictions increase consumer complaints and are emerging as a big problem for airline companies. Understanding the main reasons triggering complaints and eliminating service failures in the airline industry will be a vital strategic priority for businesses, while reviewing the dimensions of service quality during the COVID-19 pandemic provides an excellent opportunity for academic literature. In this study, 10,594 complaints against two major airlines that offer full-service and low-cost options were analyzed with the Latent Dirichlet Allocation algorithm to categorize them by essential topics. Results provide valuable information for both. Furthermore, this study fills the gap in the existing literature by proposing a decision support system to identify significant service failures through passenger complaints in the airline industry utilizing e-complaints during an unusual situation such as the COVID-19 pandemic.

Proposed information retrieval systems using LDA topic modeling for answer finding of COVID 19 pandemic: A brief survey of approaches and techniques

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA. © 2023 Author(s).

A Practical and Empirical Comparison of Three Topic Modeling Methods using a COVID-19 Corpus: LSA, LDA, and Top2Vec

This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed since the 1980s namely, latent semantic indexing or analysis (LSI/LSA), probabilistic LSI/LSA (pLSI/pLSA), naïve Bayes, the Author-Recipient-Topic (ART), Latent Dirichlet Allocation (LDA), Topic Over Time (TOT), Dynamic Topic Models (DTM), Word2Vec, Top2Vec and \variation and combination of these techniques. For researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm-Top2Vec- with two of the most conventional and frequently-used methodologies-LSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused s. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers. © 2023 IEEE Computer Society. All rights reserved.

Understanding telemedicine service users' perceptions: A text mining analysis on social media discussion

Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study aims to understand telemedicine users' perceptions of their care services and identify the aspects of telemedicine that can be improved to enhance users' experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. © 2023 IEEE Computer Society. All rights reserved.

Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.

COVID-19 vaccine rejection causes based on Twitter people's opinions analysis using deep learning.

According to the World Health Organization, vaccine hesitancy was one of the ten major threats to global health in 2019, including the COVID-19 vaccine. The availability of vaccines does not always mean utilization. This is because, people have less confidence in vaccines, which resulted in vaccination hesitancy and developing global decline in vaccine intake and has caused viral disease outbreaks worldwide. Therefore, there is a need to understand people's perceptions about the COVID-19 vaccine to help the manufacturing companies of the vaccine to improve their marketing strategy based on the rejection causes. In this paper, we used multi-class Sentiment Analysis to classify people's opinions from extracted tweets about COVID-19 vaccines, using firstly different Machine Learning (ML) classifiers such as Logistic Regression (LR), Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbors, Decision Tree (DT), Multinomial Naïve Bayes, Random Forest and Gradient Boosting and secondly various Deep Learning (DL) models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), RNN-LSTM and RNN-GRU. Then, we investigated the analysis of the negative tweets to identify the causes of rejection using the Latent Dirichlet Allocation (LDA) technique. Finally, we classified these negative tweets according to the rejection causes for all the vaccines using the same selected ML and DL models. The result of SA showed that DT gives the best performance with an accuracy of 92.26% and for DL models, GRU achieved 96.83%. Then, we identified five causes: Lack of safety, Side effect, Production problem, Fake news and Misinformation, and Cost. Furthermore, for the classification of the negative tweets according to the identified rejection causes, the LR achieved the best result with an accuracy of 89.97%. For DL models, the LSTM model showed the best result with an accuracy of 91.66%.

Sequential Topic Modelling: A Case Study on One Health Conversation on Twitter

In the past few years, HIV, SARS, cryptococcal meningoencephalitis, and COVID-19 have been worsening. The world is exterminated by pandemic COVID-19, causing tremendous death tolls, economic chaos, and social disruptions. Since the COVID-19 pandemic, the wildlife trade has been seriously re-evaluated. Twitter, as a social media platform, can be a challenging place to collect data in the form of tweets that are currently attracting the attention of many people. Nevertheless, human beings find it relatively difficult to extract latent information from a set of texts to generate particular topics. The process of evaluating the topic model started with understanding its importance. As a next step, we reviewed existing methods for topic coherence, along with the available measures of topic coherence. In order to establish a baseline coherence score, we used Gensim to implement a default Latent Dirichlet Allocation (LDA) model and discuss ways to optimize the LDA hyperparameters. © 2022 IEEE.

Detecting the research structure and topic trends of social media using static and dynamic probabilistic topic models

PurposeA huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.Design/methodology/approachThis study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.FindingsThis paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.Originality/valueThis study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.

Digital transformation in tourism: bibliometric literature review based on machine learning approach

Purpose: This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic. Design/methodology/approach: In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling. Findings: The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic;the largest ones are smart analytics, marketing strategies and sustainability. Originality/value: To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis. © 2023, Peter Madzík, Lukáš Falát, Lukáš Copuš and Marco Valeri.

Computational vs. qualitative: analyzing different approaches in identifying networked frames during the Covid-19 crisis

Despite the increasing adaption of automated text analysis in communication studies, its strengths and weaknesses in framing analysis are so far unknown. Fewer efforts have been made to automatic detection of networked frames. Drawing on the recent developments in this field, we harness a comparative exploration, using Latent Dirichlet Allocation (LDA) and a human-driven qualitative coding process on three different samples. Samples were comprised of a dataset of 4,165,177 million tweets collected from Iranian Twittersphere during the Coronavirus crisis, from 21 January, 2020 to 29 April, 2020. Findings showed that while LDA is reliable in identifying the most prominent networked frames, it misses to detects less dominant frames. Our investigation also confirmed that LDA works better on larger datasets and lexical semantics. Finally, we argued that LDA could give us some primary intuitions, but qualitative interpretations are indispensable for understanding the deeper layers of meaning. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Exploring mobile banking adoption and service quality features through user-generated content: the application of a topic modeling approach to Google Play Store reviews

PurposeThe primary purpose of this research is to analyze the online user reviews, where real customer experiences can be observed, with text mining and machine learning approaches, which are seen as a gap in the related literature. This study aims to compare the latent themes uncovered by the topic modeling approach with studies focused on both mobile banking (m-banking) adaptation and service quality features, suggest new aspects and examine the effect of latent topics on customer satisfaction.Design/methodology/approachThis study analyzed 21,526 reviews posted by customers of private and state banks operating in Türkiye. An unsupervised machine learning method, Latent Dirichlet algorithm (LDA), was conducted to reveal topics, and the distribution of all reviews was visualized with the t-SNE algorithm. Random Forest, logistic regression, k-nearest neighbors (kNN) and Naive Bayes algorithms were utilized to predict user satisfaction through the given score.FindingsIn total, 11 topics were revealed by considering user reviews based on their experience. Among these topics, perceived usefulness and convenience and time-saving are much more important in the scoring given to m-banking apps. Furthermore, in more detail, seven topics have been identified related to technical and security problems related to m-banking apps.Originality/valueThis paper is a pioneer study regarding the method used and sample size reached in the m-banking literature. The findings also provide fresh insight into the post-Covid-19 era, both academically and practically, by providing new features for mobile bank adoption.

Quantifying investor narratives and their role during COVID-19

This paper elicits and quantifies narratives from open-ended surveys sent daily to US stockholders during the first wave of the COVID-19 pandemic. Using textual analysis, we extract 13 narratives and measure their prevalence over time. A validation analysis confirms the behavioral and economic relevance of the retrieved narratives. Moreover, we find that the narratives contain predictive information for future excess stock and bond returns, and this predictability remains when controlling for contemporaneous information stemming from news and social media. Finally, we find evidence that political identity is reflected in the narrative tone. © 2023 John Wiley & Sons Ltd.

Analysis of Topic and Sentiment Trends in Customer Reviews before and after Covid-19 Pandemic

The Covid-19 pandemic has impacted many people's lives. Many researches have studied the impact of the pandemic on customer opinion change regarding services, yet there are still few researches regarding the change towards products. As a product category that experienced a significant increase in sales since the pandemic began, headphones have become a suitable product category to analyze the change. To analyze the change, this paper aims to discover the topics that customers discuss in their reviews. Latent Dirichlet Allocation (LDA) is selected as the topic modeling method to obtain the topics (i.e., aspects of a product) that are discussed in the customer reviews. In the case study, six topics that are discussed by customers are discovered, i.e., Durability Issues, Usage Contexts, Noise Cancellation, Features, Quality, and Customer Service. The monthly proportion of sentences that discuss a topic provides the topic trend. Among those six topics, the discussion about the Usage Contexts topic has increased since the beginning of the pandemic, while the other topics do not show a clear trend related to the pandemic. SentiWordNet is selected as the sentiment analysis method to capture the positive and negative sentiment towards the topics. Among the six topics, the Durability Issues and Noise Cancellation topics showed an improved sentiment after the pandemic began, while the sentiment for Usage Contexts, Features, and Quality topics worsened. Future research may be suggested to explain the worsening trend for those topics, especially the Usage Contexts topic that gained significant negativity after the pandemic began. © 2022 IEEE.

Digital News Transformation on Education in the Most Affected Country by COVID-19 Using the Topic Modeling and Sentiment Analysis

This study focuses on examining US newspaper articles regarding education from the two major news channels the New York Times (NYT) (N=29.682) and Washington Post (WST) (N=44.308) in period 1 January, 2020 - 19 March, 2021, and splitting them into three stages. We employed Latent Dirichlet Allocation topic modeling and sentiment analysis to depict the overall picture of the data set. Our method flow chart included start, data preparing, data analysis, and result. We used Python to call Google API to calculate the sentiment analysis score. There is a difference in the frequency of the occurrences of the education theme in NYT and WST in the three stages, where NYT relatively dominates. Keywords related to education that appear on the NYT and WST include school, child, parent, student, child, family, feel, and home. Sentiment analysis scores on all themes in NYT and WST were generally in the neutral categories, while the direction from stage one to stage three tends to be more positive. This study could be useful to assist education policy makers in determining the right decisions in the implementation of quality education after the COVID-19 Pandemic era. © 2022 IEEE.

A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China.

Non-pharmacological interventions (NPI) such as social distancing and lockdown are essential in preventing and controlling emerging pandemic outbreaks. Many countries worldwide implemented lockdowns during the COVID-19 outbreaks. However, due to the lack of prior experience and knowledge about the pandemic, it is challenging to deal with short-term polices decision-making due to the highly stochastic and dynamic nature of the COVID-19. Thus, there is a need for the exploration of policy decision analysis to help agencies to adjust their current policies and adopt quickly. In this study, an analytical methodology is developed to analysis urban transport policy response for pandemic control based on social media data. Compared to traditional surveys or interviews, social media can provide timely data based on the feedback from public in terms of public demands, opinions, and acceptance of policy implementations. In particular, a sentiment-aware pre-trained language model is fine-tuned for sentiment analysis of policy. The Latent Dirichlet Allocation (LDA) model is used to classify documents, e.g., posts collected from social media, into specific topics in an unsupervised manner. Then, entropy weights method (EWM) is used to extract public policy demands based on the classified topics. Meanwhile, a Jaccard distance-based approach is proposed to conduct the response analysis of policy adjustments. A retrospective analysis of transport policies during the COVID-19 pandemic in Wuhan, China is presented using the developed methodology. The results show that the developed policymaking support methodology can be an effective tool to evaluate the acceptance of anti-pandemic policies from the public's perspective, to assess the balance between policies and people's demands, and to further perform the response analysis of a series of policy adjustments based on online feedback.

Latent Dirichlet allocation topic modeling of free-text responses exploring the negative impact of the early COVID-19 pandemic on research in nursing.

AIM: To derive latent topics from free-text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic-based and clinical-based researchers. METHODS: We performed a secondary analysis of free-text responses from a cross-sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic-based and clinical-based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic-associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. RESULTS: A total of 201 respondents (163 academic-based and 38 clinical-based researchers) provided free-text responses. LDA identified eight and three latent topics for the academic-based and clinical-based researchers, respectively. While PCA re-grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was "barriers to conducting research," with the remaining themes differing between the groups. CONCLUSIONS: Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free-text responses about the negative impact of the pandemic between academic-based and clinical-based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.

A new LDA formulation with covariates

The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.