Use Of Latent Dirichlet Allocation Research Articles

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

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

Machine learning review of hand surgery literature

Background: Latent Dirichlet Allocation is an artificial intelligence model which processes text into topics, and has had broad application in medicine, political science, and engineering. As the orthopedic hand literature continues to grow, such technology may have value in efficiently conducting identifying trends and conducting systematic reviews. The purpose of this study is to demonstrate the use of Latent Dirichlet Allocation and machine learning to review literature and summarize the past 21 yr of hand surgery research. Methods: All original research articles published in the Journal of Hand Surgery (American), Journal of Hand Surgery (European), Hand, Journal of Bone and Joint Surgery (JBJS), Clinical Orthopaedics and Related Research (CORR), Journal of the American Academy of Orthopaedic Surgeons (JAAOS) and Plastic and Reconstructive Surgery (PRS) from 2000-2021 were analyzed using Latent Dirichlet Allocation, generating 50 topics which were then ranked by popularity and trended over the previous 21 yr. Results: Research article abstracts totaling 11,501 from 2000-2020 were extracted and analyzed to create 50 topics. Conclusion: This is the first study of its kind to utilize machine learning models for reviewing the hand surgery literature. Machine learning possesses the ability to rapidly process a large body of test and assess the current state of research and trends or research topics, which can aid clinicians and researchers in time-intensive tasks to provide clues that will promote areas of further study.

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets.

With the rise of social media platforms, sharing reviews has become a social norm in today's modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald's, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users’ continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took “Himalayan FM” as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users’ willingness to continue use, through mining user comment data on the knowledge payment platform; Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users’ willingness to continuous use, while perceived cost has a direct negative impact on users’ willingness to continue use; (2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users’ willingness to continue use indirectly; (3) users’ perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users’ willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users’ willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.

Does hotel customer satisfaction change during the COVID-19? A perspective from online reviews

The coronavirus disease (COVID-19) has impacted the hotel industry in all aspects. However, the changes in hotel customer satisfaction deserve additional attention. Using online hotel reviews, this study explores the difference between the influencing factors of customer satisfaction before and after the COVID-19 outbreak. By use of Latent Dirichlet Allocation and sentiment analysis, factors that affect customer satisfaction and their emotional strength before and after the COVID-19 outbreak are extracted. Then, multiple regression models are established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods. According to the aforesaid study, hotel customer satisfaction and its influencing factors have changed significantly during the pandemic; hotel customer satisfaction during the pandemic is mainly influenced by service quality. Accordingly, strategies are proposed for hotel managers to improve their customer satisfaction during the COVID-19.

Assessing systemic risk in financial markets using dynamic topic networks

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015–2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.

Sentiment-Topic Dynamics on COVID-19 Post-Traumatic Stress Disorder using Online Published Researches

This paper will analyze the COVID-19 Posttraumatic Stress Disorder (PSTD) researches retrieved from PubMed and other online sources to determine the impact of the COVID-19 pandemic on mental health leading to PSTD. The Sentiment Classifier and Latent Dirichlet Allocation (LDA) were used to detect the topic relevant to post-traumatic stress disorder. The finding revealed a negative sentiment while the use of Latent Dirichlet Allocation determined the critical issue “COVID”. The results suggest further study of the stress and other traumatic experiences bought by the COVID.

Understanding the Temporal Effects on Tweetcussion of COVID-19 Vaccine.

In the fight against COVID‐19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID‐19. Specifically, since the COVID‐19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID‐19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine‐related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.

Circular Economy on Construction and Demolition Waste: A Literature Review on Material Recovery and Production.

Construction and demolition waste (CDW) accounts for at least 30% of the total solid waste produced around the world. At around 924 million tons in the European Union in 2016 and 2.36 billion tons in China in 2018, the amount is expected to increase over the next few years. Dumping these wastes in sanitary landfills has always been the traditional approach to waste management but this will not be feasible in the years to come. To significantly reduce or eliminate the amount of CDW being dumped, circular economy is a possible solution to the increasing amounts of CDW. Circular economy is an economic system based on business models which replaces the end-of-life concept with reducing, reusing, recycling, and recovering materials. This paper discusses circular economy (CE) frameworks—specifically material recovery and production highlighting the reuse and recycling of CDW and reprocessing into new construction applications. Likewise, a literature review into recent studies of reuse and recycling of CDW and its feasibility is also discussed to possibly prove the effectivity of CE in reducing CDW. Findings such as effectivity of recycling CDW into new construction applications and its limitations in effective usage are discussed and research gaps such as reuse of construction materials are also undertaken. CE and recycling were also found to be emerging topics. Observed trends in published articles as well as the use of latent Dirichlet allocation in creating topic models have shown a rising awareness and increasing research in CE which focuses on recycling and reusing CDW.

Understanding consumers’ online fashion renting experiences: A text-mining approach

With the increasing demand for fashion products, the textile and apparel industry is facing huge challenges in resource management and environmental regulations. Fashion renting provides an option to reuse clothing products. Besides that, it also fulfills an individual’s fashion needs while reducing the production of new clothes. Though some studies have been done to examine consumers’ opinions of fashion renting, most of these studies employed survey methods and did not utilize actual data of consumers’ real renting experiences. The purpose of this study is to evaluate consumers’ actual renting experiences and to identify the motivations and barriers for those consumers to rent fashion products. A data-mining approach was applied in this study. Consumers’ comments on renting experiences from three fashion rental companies, Rent the Runway (RTR), Gwynnie Bee (GB), and Bag Borrow Steal (BBS), were collected as a reliable data source to dig into and identify consumers’ motivations and concerns. Based on the theory of customer value, both benefits and costs for fashion renting were discovered. In addition, a comparison of the three fashion rental companies was also discussed. This study is the first attempt to use a data-mining method to thoroughly investigate the benefits and costs of fashion online renting through real consumers’ feedback of three different types of rental companies. It provides an in-depth text analysis of consumers’ online fashion renting experiences through the use of Latent Dirichlet Allocation (LDA) topic modeling and word co-occurrence networks.

Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke: A Topic Modeling Approach.

BackgroundThirdhand smoke has been a growing topic for years in China. Thirdhand smoke (THS) consists of residual tobacco smoke pollutants that remain on surfaces and in dust. These pollutants are re-emitted as a gas or react with oxidants and other compounds in the environment to yield secondary pollutants.ObjectiveCollecting media reports on THS from major media outlets and analyzing this subject using topic modeling can facilitate a better understanding of the role that the media plays in communicating this health issue to the public.MethodsThe data were retrieved from the Wiser and Factiva news databases. A preliminary investigation focused on articles dated between January 1, 2013, and December 31, 2017. Use of Latent Dirichlet Allocation yielded the top 10 topics about THS. The use of the modified LDAvis tool enabled an overall view of the topic model, which visualizes different topics as circles. Multidimensional scaling was used to represent the intertopic distances on a two-dimensional plane.ResultsWe found 745 articles dated between January 1, 2013, and December 31, 2017. The United States ranked first in terms of publications (152 articles on THS from 2013-2017). We found 279 news reports about THS from the Chinese media over the same period and 363 news reports from the United States. Given our analysis of the percentage of news related to THS in China, Topic 1 (Cancer) was the most popular among the topics and was mentioned in 31.9% of all news stories. Topic 2 (Control of quitting smoking) was related to roughly 15% of news items on THS.ConclusionsData analysis and the visualization of news articles can generate useful information. Our study shows that topic modeling can offer insights into understanding news reports related to THS. This analysis of media trends indicated that related diseases, air and particulate matter (PM2.5), and control and restrictions are the major concerns of the Chinese media reporting on THS. The Chinese press still needs to consider fuller reports on THS based on scientific evidence and with less focus on sensational headlines. We recommend that additional studies be conducted related to sentiment analysis of news data to verify and measure the influence of THS-related topics.

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.

BackgroundMedication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance.ObjectiveThe aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes.MethodsWe focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment.ResultsStarting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage.The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844).ConclusionsTopic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

A Decision Support System for Inbound Marketers: An Empirical Use of Latent Dirichlet Allocation Topic Model to Guide Infographic Designers

Infographic is a type of information presentation that inbound marketers use. I suggest a method that can allow the infographic designers to benchmark their design against the previous viral infographics to measure whether a given design decision can help or hurt the probability of the design becoming viral.

Discourses and Disciplines in the Enlightenment: Topic Modeling the French Encyclopédie

This paper describes the use of Latent Dirichlet Allocation (LDA), or topic modeling, to explore the discursive makeup of the18th-century Encyclopedie of Denis Diderot and Jean le Rond d’Alembert (1751-1772). Expanding upon previous work modeling the Encyclopedie’s ontology, or classification scheme, we examine the abstractions used by its editors to visualize the various ‘systems’ of knowledge that the work proposes, considered here as heuristic tools for navigating the complex information space of the Encyclopedie. Using these earlier experiments with supervised machine learning models as a point of reference, we introduce the notion of topic modeling as a ‘discourse analysis tool’ for Enlightenment studies. In so doing, we draw upon the tradition of post-structuralist French discourse analysis, one of the first fields to embrace computational approaches to discursive text analysis. Our particular use of LDA is thus aimed primarily at uncovering inter-disciplinary ‘discourses’ in the Encyclopedie that run alongside, under, above, and through the original classifications. By mapping these discourses and discursive practices we can begin to move beyond the organizational (and physical) limitations of the print edition, suggesting several possible avenues of future research. These experiments thus attest once again to the enduring relevance of the Encyclopedie as an exemplary Enlightenment text. Its rich dialogical structure, whether studied using traditional methods of close reading or through the algorithmic processes described in this paper, is perhaps only now coming fully to light thanks to recent developments in digital resources and methods.

Comparing digital traces of modern travellers to journeys of two 18th-19th century British poets

The growth of interest in georeferenced media brings with it a wealth of possibilities for exploring digital media, and importantly, for advancing research concerned with domain driven research questions. In this paper, we compare previous work, which extracted and analysed the spatial traces of two 18th-19th century poets in the English Lake District with modern data digital traces in the form of Flickr images. We explore the semantics of the modern day data through use of Latent Dirichlet Allocation, and analyse the extent to which modern day tourism mimics (or indeed follows) the foundations laid by Samuel Coleridge and Thomas Gray. Our results show that tourists, just like the poets, describe mountain landscapes from below and visit popular locations commonly perceived as beautiful.

Authorship attribution based on a probabilistic topic model

This paper describes, evaluates and compares the use of Latent Dirichlet allocation (LDA) as an approach to authorship attribution. Based on this generative probabilistic topic model, we can model each document as a mixture of topic distributions with each topic specifying a distribution over words. Based on author profiles (aggregation of all texts written by the same writer) we suggest computing the distance with a disputed text to determine its possible writer. This distance is based on the difference between the two topic distributions. To evaluate different attribution schemes, we carried out an experiment based on 5408 newspaper articles (Glasgow Herald) written by 20 distinct authors. To complement this experiment, we used 4326 articles extracted from the Italian newspaper La Stampa and written by 20 journalists. This research demonstrates that the LDA-based classification scheme tends to outperform the Delta rule, and the χ2 distance, two classical approaches in authorship attribution based on a restricted number of terms. Compared to the Kullback–Leibler divergence, the LDA-based scheme can provide better effectiveness when considering a larger number of terms.

Dr. Guarneri Honored at Bravewell Leadership Event

The coronavirus disease (COVID-19) has impacted the hotel industry in all aspects. However, the changes in hotel customer satisfaction deserve additional attention. Using online hotel reviews, this study explores the difference between the influencing factors of customer satisfaction before and after the COVID-19 outbreak. By use of Latent Dirichlet Allocation and sentiment analysis, factors that affect customer satisfaction and their emotional strength before and after the COVID-19 outbreak are extracted. Then, multiple regression models are established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods. According to the aforesaid study, hotel customer satisfaction and its influencing factors have changed significantly during the pandemic; hotel customer satisfaction during the pandemic is mainly influenced by service quality. Accordingly, strategies are proposed for hotel managers to improve their customer satisfaction during the COVID-19.