Latent Dirichlet Allocation Approach Research Articles

A Latent Dirichlet Allocation approach to understanding students’ perceptions of Automated Writing Evaluation

Automated writing evaluation (AWE) has shown promise in enhancing students’ writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students’ perceptions of the MI Write AWE system. Students reported their perceptions of MI Write's usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students’ comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students’ ratings of MI Write's usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students’ perspectives of AWE.

Uncovering the themes and trends in crowdfunding research using Latent Dirichlet Allocation

AbstractCrowdfunding (CF) has become a significant force in the entrepreneurial landscape, offering an innovative alternative to traditional financing channels for startups and projects. As the field expands, it is crucial to systematically analyze the existing literature to identify key themes, patterns, and emerging areas of interest. To achieve this goal, this study investigates the CF literature using latent Dirichlet allocation (LDA)-based topic modeling based on 1,678 publications extracted from the Scopus database. The review reveals significant growth in CF research, with top journals spanning diverse disciplines. Eight main topics are identified, including CF campaign success and financing, donation-based CF, social effects of CF, entrepreneurial projects and rewards in CF, financial and fintech aspects of CF, CF project success and performance, P2P lending models and credit risk assessment, and equity CF and venture capital. Several research directions are suggested for each topic to advance the CF field. The theoretical and practical implications are also discussed. To the authors’ best knowledge, this study represents the first systematic analysis of the CF literature using the LDA approach, offering a comprehensive and up-to-date overview of this field and highlighting emerging areas of interest and potential research directions.

Analysis of pedestrian-related crossing behavior at intersections: A Latent Dirichlet Allocation approach

Analysis of pedestrian-related crossing behavior at intersections: A Latent Dirichlet Allocation approach

How open innovation specialists contribute to corporate sustainability and responsibility: A latent Dirichlet allocation approach

AbstractThis study examines the job postings for open innovation (OI) specialists to determine a universal archetype of what competencies and tasks are requested from said professionals and their implications for corporate sustainability and responsibility. This research uses Bayesian statistics and latent Dirichlet allocation (LDA) topic modeling to measure multiple dimensions of the 341 sampled job postings. Our empirical findings unveil the pivotal role that OI specialists have in engaging with stakeholders and monitoring OI dynamics. Multiple dimensions are expected from OI specialists, addressing a multitude of concerns, such as the environment and technologies. Moreover, this study underlines the need for OI specialists to comprehend both internal and external stakeholders' needs. This research contributes to the literature as follows. First, it underlines the value of topic modeling analysis in job profiling research. Second, it bridges existing knowledge gaps on OI specialists' competencies and roles with empirical evidence obtained from a global dataset. Third, it outlines the current market expectations and requirements for OI specialists, which is useful to both candidates and companies.

Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants.

Given the high amount of information available on social media, the paper explores the degree of vaccine hesitancy expressed in English tweets posted worldwide during two different one-month periods of time following the announcement regarding the discovery of new and highly contagious variants of COVID-19-Delta and Omicron. A total of 5,305,802 COVID-19 vaccine-related tweets have been extracted and analyzed using a transformer-based language model in order to detect tweets expressing vaccine hesitancy. The reasons behind vaccine hesitancy have been analyzed using a Latent Dirichlet Allocation approach. A comparison in terms of number of tweets and discussion topics is provided between the considered periods with the purpose of observing the differences both in quantity of tweets and the discussed discussion topics. Based on the extracted data, an increase in the proportion of hesitant tweets has been observed, from 4.31% during the period in which the Delta variant occurred to 11.22% in the Omicron case, accompanied by a diminishing in the number of reasons for not taking the vaccine, which calls into question the efficiency of the vaccination information campaigns. Considering the proposed approach, proper real-time monitoring can be conducted to better observe the evolution of the hesitant tweets and the COVID-19 vaccine hesitation reasons, allowing the decision-makers to conduct more appropriate information campaigns that better address the COVID-19 vaccine hesitancy.

Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies’ social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers’ work and family, and (e) users’ action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week’, ‘safety first’, and ‘construction team’, (b) the largest clusters were ‘safety training’, ‘occupational health and safety administration’, and ‘health and safety environment’, (c) the most active users were ‘Parallel Consultancy Ltd.’, ‘Pike Consulting Group’, and ‘Global Training Canada’, and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.

Circular-Economy-Based Approach to Utilizing Cardboard in Sustainable Building Construction

Circular-economy-based sustainability approaches in construction are gaining wide acceptance due to the volume of waste generation and increasing demand for natural materials. Propelled by the recent timber shortage in Australia and the issues of waste management of cardboard, this study aims to analyse the possibilities of using cardboard as a construction material, based on its initial strength and multiple recycling options. A systematic review of research papers published in the last 40 years has been undertaken using a single keyword search to select the database. The review is presented in terms of the characteristics of the cardboard, dimensional stability, durability, structural strength, design, and analysis of cardboard. Recurring themes are evaluated using a latent Dirichlet allocation approach to identify the factors that ascertain the suitability of cardboard. Analysis reveals that despite certain constraints, such as water absorption and fire resistance, cardboard can be used as a replacement for timber by overcoming such limitations. This observation has benefits for the construction industry and the recycling industry. This study found that cardboard adheres to the circular economy principles, which should inspire policymakers. The paper concludes by highlighting the current circumstances and scientific challenges that impede the usage of cardboard in construction and recommends potential works needed to address these challenges for the benefit of practitioners and researchers.

Crowd inputs in reward-based and equity-based crowdfunding: a latent Dirichlet allocation approach on their potential for innovation

PurposeCrowdfunding platforms are important innovations that allow nascent entrepreneurs to gain access to financial resources and crowd inputs to better refine and develop their business idea. The purpose of this paper is to investigate user-generated content (UGC) from both reward-based and equity-based crowdfunding platforms, in order to determine its implications for open and user innovation.Design/methodology/approachA total sample of 200 most funded technology products was extracted from four distinct crowdfunding platforms. A latent Dirichlet allocation (LDA) analysis was performed in an attempt to identify critical latent factors. The analysis was carried out through the theoretical lens of innovation literature, in an attempt to uncover the implications for open and user innovation.FindingsThe authors were able to highlight the implications of crowd inputs for open and user innovation, as backers provided nascent entrepreneurs with several types of feedback, ranging from product co-development to strategy and marketing. Furthermore, the study provided an overview of the key differences emerging between reward-based and equity-based crowdfunding platforms in terms of crowd inputs.Research limitations/implicationsThe present study features intrinsic limitations of the LDA approach being adopted. More specifically, it only provides a “snapshot” in time of the current sample, rather than investigating its development over time.Practical implicationsThe present study solidifies the value of UGC as a resource to mine for trends and feedback.Originality/valueThe study contributes to both the innovation literature and the crowdfunding literature. It bridges several gaps found in both literature streams, by providing empirical evidence to test and verify pre-existing exploratory research.

Empirical identification of the chief digital officer role: A latent Dirichlet allocation approach

This study examines the global demand for Chief Digital Officers (CDOs) to determine a universal CDO archetype in terms of competencies and tasks. It uses Bayesian statistics and Latent Dirichlet Allocation (LDA) topic modeling to measure multiple dimensions in a sample of 518 job postings for CDO positions. Findings show the hybrid nature of newly appointed CDOs, who feature a mixture of both business administration and technological skills. Further, the study highlights the pivotal role of CDOs in terms of strategic change in companies. The study has three major contributions. First, it showcases the value of LDA in job profiling research. Second, it bridges the existing knowledge gaps in CDO literature with empirical evidence from a global dataset and identifies a core CDO profile based on data extracted through LDA. Third, it illustrates the current market requirements for CDO positions, which is useful to both companies and candidates.

Understanding the Scientific Topics in the Chinese Government’s Communication about COVID-19: An LDA Approach

The communication of scientific topics can play a key role in the fight against misinformation and has become an important component of governments’ communication regarding COVID-19. This study reviewed the Chinese government’s COVID-19 information sources and identified the patterns of science communication models within them. A corpus of science-related content was collected and coded from 1521 news briefings announced by the Chinese government. An LDA (latent Dirichlet allocation) topic model, correlation analysis, and ANOVA were used to analyze the framing of the scientific topics and their social environmental characteristics. The major findings showed the following: (1) The frames in the Chinese government’s communication of scientific topics about COVID-19 had three purposes—to disseminate knowledge about prevention and control, epidemiological investigations, and the public’s personal health; to make the public understand scientific R&D in Chinese medicine, enterprises, vaccines, treatment options, and medical resources; and to involve citizens, communities, and enterprises in scientific decision making. (2) The frames were correlated with the public and media concerns. (3) The frames varied with the different levels of officials, different types of government agencies, different income regional governments, and different severity levels of the epidemic. (4) The topics concerning sustainability science were more correlated with public and media concern. In addition, we propose several suggestions for building sustainable communication approaches during the pandemic.

Incident Duration Time Prediction Using Supervised Topic Modeling Method

Accurate prediction of the duration of traffic incidents is one of the most prominent prerequisites for effective implementation of proactive traffic incident management strategies. This paper presents a novel method for immediate prediction of traffic incident duration using an emerging supervised topic modeling. The proposed method employs natural language processing techniques for semantic text analysis of the text-based incident traffic incident dataset. The model applies the labeled latent Dirichlet allocation approach, and it is trained using 1,466 incident records collected by the Korea Expressway Corporation from 2016 to 2019. For training purposes, the proposed method divides the incidents into two groups based on the incident duration: incidents shorter than 2 h and incidents lasting 2 h or longer, following the incident management guidelines of the Federal Highway Administration Manual on Uniform Traffic Control Devices for Streets and Highways (2009). The model is tested with randomly selected incident records that were not used for the model training. The results demonstrate overall prediction accuracies of approximately 74% for incidents lasting up to 2 h, and 82% for incidents lasting 2 h or longer.

Exploring conversation topics in conversational artificial intelligence–based social mediated communities of practice

This study utilized ecologically valid social media data to identify motivations and relevant topics regarding the interaction with conversational artificial intelligence (AI) in a natural setting through investigating user conversations on Reddit, a social mediated community of practice. By applying the latent Dirichlet allocation approach, this study extracted conversation topics in six subreddit communities among users of AI–powered virtual assistants (Apple Siri, Amazon Alexa, and Google Assistant) and the corresponding smart speakers (Apple HomePod, Amazon Echo, and Google Home), and investigated changes in the conversation topics over time. Findings showed six themes of conversation topics, i.e., functional gratification, hedonic gratification, social gratification, settings, problems encountered, and connections between devices. A large volatility of the conversation topics over time was found. The results implied that members in subreddit communities share their motivations for interacting with conversational AI and collaboratively discuss the relevant issues and problem solving to learn how to practice better.

A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model

There is a growing interest in topic modeling to decipher the valuable information embedded in natural texts. However, there are no studies training an unsupervised model to automatically categorize the social networks (SN) messages according to personality traits. Most of the existing literature relied on the Big 5 framework and psychological reports to recognize the personality of users. Furthermore, collecting datasets for other personality themes is an inherent problem that requires unprecedented time and human efforts, and it is bounded with privacy constraints. Alternatively, this study hypothesized that a small set of seed words is enough to decipher the psycholinguistics states encoded in texts, and the auxiliary knowledge could synergize the unsupervised model to categorize the messages according to human traits. Therefore, this study devised a dataless model called Seed-guided Latent Dirichlet Allocation (SLDA) to categorize the SN messages according to the PEN model that comprised Psychoticism, Extraversion, and Neuroticism traits. The intrinsic evaluations were conducted to determine the performance and disclose the nature of texts generated by SLDA, especially in the context of Psychoticism. The extrinsic evaluations were conducted using several machine learning classifiers to posit how well the topic model has identified latent semantic structure that persists over time in the training documents. The findings have shown that SLDA outperformed other models by attaining a coherence score up to 0.78, whereas the machine learning classifiers can achieve precision up to 0.993. We also will be shared the corpus generated by SLDA for further empirical studies.

Deep Learning for Latent Events Forecasting in Content Caching Networks

A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geo-aware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: 1) the proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests; 2) the proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data; 3) the perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach; and 4) evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

SPUCL (Scientific Publication Classifier): A Human-Readable Labelling System for Scientific Publications

To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach.

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions.

In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.

Entrepreneurial crowdfunding backer motivations: a latent Dirichlet allocation approach

PurposeThis study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd's decision to support a crowdfunding project.Design/methodology/approachBacker's comments from a sample of the top 100 most funded technology product projects on KickStarter were collected. A latent Dirichlet allocation (LDA) analysis strategy was adopted to investigate critical motivational factors. Three experts mapped those factors to the known theoretical constructs of social exchange theory (SET).FindingsAlthough backers are motivated by value, they are also motivated by far less tangible social factors including trust and a feeling of psychological ownership. Findings suggest that the crowd is far more than a passive group of investors or customers and should be viewed as participatory stakeholders. This study serves as guidance for project owners hoping to motivate the crowd and for future investigators examining backer motivations in other types of crowdsourcing projects.Research limitations/implicationsOnline chatter in the form of user-generated comments is an excellent data source for researchers to mine for value and meaning.Practical implicationsGiven strong feelings of psychological ownership, project owners should actively engage the crowd and solicit the crowd for advice and help in order to motivate them.Originality/valueThe study presents the first empirical exploration of backer motivations using LDA guided by theory and the knowledge of experts. A framework of latent motivational factors is proposed.

Understanding Service Providers’ Competency in Knowledge-Intensive Crowdsourcing Platforms: An LDA Approach

Knowledge-intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor-intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three-level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best-Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling.

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.