Topic Modeling Techniques Research Articles

Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms

Online word-of-mouth (WOM) has attracted considerable attention from researchers due to its abundant information on customer perceptions that drive product and service improvement. This study develops a novel weighted service quality (WSQ) metric derived from online customer opinions, leveraging the importance-performance analysis framework. Data collected from social and non-social online platforms confirms that this WSQ approach outperforms the widely used average sentiment score approach and significantly predicts the industry service quality standard, Airline Quality Rating (AQR). In addition, the WSQ metric derived from social media proves to be a more vital indicator for AQR than that derived from a non-social online platform. A significant difference in topic distributions was also identified between consumer opinions from social media and non-social online platforms. Our study makes several crucial contributions to the service quality literature on employing online WOM using sentiment analysis and topic modeling techniques.

Leveraging newspapers to understand urban issues: A longitudinal analysis of urban shrinkage in Detroit

Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to longer-term urban problems that take decades to emerge. Concerning longer-term coverage, newspapers, which are increasingly becoming digitized, provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utility of newspapers for urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to how Detroit shrinks. For example, side effects of 2007 to 2009 economic recessions on Detroit’s automobile industry, local employment status, and the housing market.

Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell

Gathering information about cyber threats from various sources can help organisations improve proactive cyber defense and mitigate potential cyber attacks. Recently, Twitter has shown to be beneficial in providing timely Cyber Threat Intelligence (CTI) concerning cyber threats, software vulnerabilities and exploits. However, manually identifying and investigating useful insights, patterns, and trends from abundant unstructured tweets is difficult. This work proposes an end-to-end data-driven framework to collect, analyze, and monitor tweets using unsupervised topic modeling techniques. A novel visualization technique is also proposed to monitor the dynamic topic trends over time, offering an interpretable way to gain insights into a topic's lifecycle. A case study is conducted on the Log4shell vulnerability incident to demonstrate the applicability of the proposed framework. Experiments are carried out on a real-world Twitter dataset collected from 47 users within the CTI community. Results indicate that the proposed framework can discover emerging topics relevant to real-world cybersecurity incidents, with Log4Shell-related topics identified before the common public disclosure date by the National Vulnerability Database (NVD). This framework can expedite the data processing workflow and visualization for cyber threat analysis, enabling organizations to identify trends and patterns that can potentially indicate a security breach or attack.

Statistical clustering of documents via stochastic blockmodels

ABSTRACT As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset. But in our work, we propose a method that utilizes both text analysis method such as topic modeling and statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the edges among the individuals and form a network and uses stochastic blockmodels (SBM) to find the communities. The power of our proposed method is demonstrated in real-world application to Amazon product review dataset.

The Use of Technology in Korean Mathematics Education: A Systematic Review

The use of educational technology has the potential to enhance teachers’ instructional quality and student achievement. The international research community has conducted various studies to address the opportunities and challenges of using technologies for teaching and learning mathematics education. However, relatively little attention has been given to identifying research topics in Korean domestic research. Therefore, the present study aims to provide a systematic literature review that identifies and compares the research topics studied nationally and internationally. The domestic literature was collected from the KCI database. We employed a topic modeling technique to analyze overall research topics and trends in technology use in mathematics education. Additionally, we compared these findings with our previous studies where we examined international research trends. This study revealed some similarities and differences in research trends between domestic and international studies and suggested implications for future studies.

Influence Relationship between Online News Articles and the Consumer Selling Price of Agricultural Products—Focusing on Onions

This study aimed to verify the influence relationship between the news articles on onions produced in Korea and the consumer selling price of onions. The analysis methods were the LDA topic modeling technique and the multiple regression analysis. As a result of the analysis, a total of eight topics were found in onion-related news articles. This study analyzed which articles out of the eight topics affected the consumer selling price of onions. As a result, Topic 1 (hypermarket onion sales-related articles), Topic 5 (onion supply and demand stabilization measures), and Topic 6 (inflation) had a statistically significant influence relationship. These results meant that as the number of hypermarket-related articles increased, the consumer selling price increased, and as the macroeconomic articles such as supply and demand stabilization measures and inflation increased, the selling price decreased. The significance of this study was that it revealed that news articles related to onions did not affect the selling price in the consumer market as a whole, and that only the articles directly related to the consumption market (distributors, macroeconomic indicators, etc.) had an effect.

Episodes and Topics in Multivariate Temporal Data

AbstractThe term ‘episode’ refers to a time interval in the development of a dynamic process or behaviour of an entity. Episode‐based data consist of a set of episodes that are described using time series of multiple attribute values. Our research problem involves analysing episode‐based data in order to understand the distribution of multi‐attribute dynamic characteristics across a set of episodes. To solve this problem, we applied an existing theoretical model and developed a general approach that involves incrementally increasing data abstraction. We instantiated this general approach in an analysis procedure in which the value variation of each attribute within an episode is represented by a combination of symbols treated as a ‘word’. The variation of multiple attributes is thus represented by a combination of ‘words’ treated as a ‘text’. In this way, the the set of episodes is transformed to a collection of text documents. Topic modelling techniques applied to this collection find groups of related (i.e. repeatedly co‐occurring) ‘words’, which are called ‘topics’. Given that the ‘words’ encode variation patterns of individual attributes, the ‘topics’ represent patterns of joint variation of multiple attributes. In the following steps, analysts interpret the topics and examine their distribution across all episodes using interactive visualizations. We test the effectiveness of the procedure by applying it to two types of episode‐based data with distinct properties and introduce a range of generic and data type‐specific visualization techniques that can support the interpretation and exploration of topic distribution.

How academic research and news media cover climate change: a case study from Chile

IntroductionClimate change has significant impacts on society, including the environment, economy, and human health. To effectively address this issue, it is crucial for both research and news media coverage to align their efforts and present accurate and comprehensive information to the public. In this study, we use a combination of text-mining and web-scrapping methods, as well as topic-modeling techniques, to examine the similarities, discrepancies, and gaps in the coverage of climate change in academic and general-interest publications in Chile.MethodsWe analyzed 1,261 academic articles published in the Web of Science and Scopus databases and 5,024 news articles from eight Chilean electronic platforms, spanning the period from 2012 to 2022.ResultsThe findings of our investigation highlight three key outcomes. Firstly, the number of articles on climate change has increased substantially over the past decade, reflecting a growing interest and urgency surrounding the issue. Secondly, while both news media and academic research cover similar themes, such as climate change indicators, climate change impacts, and mitigation and adaptation strategies, the news media provides a wider variety of themes, including climate change and society and climate politics, which are not as commonly explored in academic research. Thirdly, academic research offers in-depth insights into the ecological consequences of global warming on coastal ecosystems and their inhabitants. In contrast, the news media tends to prioritize the tangible and direct impacts, particularly on agriculture and urban health.DiscussionBy integrating academic and media sources into our study, we shed light on their complementary nature, facilitating a more comprehensive communication and understanding of climate change. This analysis serves to bridge the communication gap that commonly, exists between scientific research and news media coverage. By incorporating rigorous analysis of scientific research with the wider reach of the news media, we enable a more informed and engaged public conversation on climate change.

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets.

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines. This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets. We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques. Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period. This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine-related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.

Predicting the Evolution of Literature on Industrial Symbiosis Using Topic Modeling

The aim of this literature review is to suggest future research on industrial symbiosis. This study is based on the analysis of the latent topics related with the industrial symbiosis found in a collection of 611 scientific articles retrieved from Web of Science on 2022/3/11. Our research process applies the topic modelling technique, which allows the grouping of documents that discuss similar topics, even if these topics are not explicitly mentioned in the documents themselves The initial classification of the existing body of works made it possible to identify current trends and developments in the field and the most promising lines of future research. Specifically, we identified 12 themes, which we characterised with a short sentence rather than a single label. Among the 12 themes, we have highlighted those that are attracting increasing attention from scholars, while others are declining or remaining stable over time.

An efficient LSTM based cross domain aspect based sentiment analysis (CD-ABSA)

This research study focuses the cross-domain aspect-based sentiment analysis (CD-ABSA) for existing source domain annotation data. The CD-ABSA tries to use the valuable information in a source domain to extract aspect terms and evaluate their sentiment polarities in a target domain. It can considerably increase the usage of the source domain annotation resources while also reducing the workload of newer domain data annotation. one of the main components of the CD-ABSA is aspect extraction. In this paper, we utilized the most common topic modelling techniques: LDA and LSA to extract aspects from the reviews as it does not require labelled data. The topics are extracted from the education domain of the Course and Teacher Performance Evaluation (CTPE) dataset. In this paper, we also evaluated the different hyper-parameters on the CD-ABSA model and selected the best and optimal combination. The proposed methodology train on domain-dependent and independent word embedding that achieves CD-ABSA, in particularly end-to-end fashion. The experiment carries out on Academica dataset, which consists of students’ comments/feedback and SemEval-2014 dataset, which includes laptops and restaurants reviews. The evaluation metrics such as (precision, recall, F1 score and validation Accuracy) is considered while judging the LSTM classifier performance for CD-ABSA as a result.

Between alternative and traditional social platforms: the case of gab in exploring the narratives on the pandemic and vaccines.

The phenomenon of deplatforming intended as the removal of social media accounts because of breaking rules on mainstream platforms such as Facebook, Twitter, YouTube, and Instagram recently increased due to new terms and conditions of use of digital media, and new alternative social media platforms emerged and presented themselves as protectors of freedom expression. In this way, it becomes interesting to understand better the context of these platforms' so-called web suburbs that consist in those digital places that ≪host what we can generally call "subcultures," including fandoms, religious sects, political extremists, and subcultures≫. Since April 2020, Gab can be considered the most widespread alternative platform in Western countries, with twenty million users daily, born as Twitter and Facebook alternative social media. The alternative social media platforms are intended as other connection services between users, which is halfway between a social media and a discussion forum born to boycott the censorship actions of the main social media platforms (Meta Group, Twitter, etc.) and celebrate free speech even on controversial positions. How are sensitive topics, such as the one that concerns the skepticism related to the approvals of vaccines during the pandemic, addressed on the alternative social media platform compared to how they are dealt with on the mainstream social media platforms? This explorative work wonders about the users' points of view on vaccine concerns and the relevant differences between Gab and Facebook in addressing this topic. The empirical part of this work has been set starting from the dataset composed of Gab and Facebook content posted between March 2020 and July 2021. The posts were extracted with web scraping techniques (for Gab) and proprietary data tools (for Facebook), querying the keywords: vaccine, vaccines, anti-vax (no-vax), Covid, Covid-19, coronavirus. The collection procedure considered the different platforms' structure and their different organization of the interaction spaces. The population consisted of 8000 English writers' posts, from which 2000 posts with the highest interaction value were extracted. The dataset was analyzed using Topic Modeling, Factor, and Classification Analysis techniques. Our work's methodological output deals with comparing these social media platforms, bearing in mind their ontological objects and their algorithms' role. From the analysis emerged the differences and similarities of the social media platforms in terms of the type of content published, rates of involvement, sources of information, and directions of the considered speech. These differences have been duly highlighted by three clusters related to discourse orientation and communication approach: Conflict of views, Emotional externalization, Recommendation and practices. In addition to the type of communication and information circulating on a powerful platform such as Gab, the results help us understand the different narratives promoted on the two social media platforms and their role in the possible promotion of the same sentiment, opinions, and ideological polarization.

A hybrid DEMATEL and social network analysis model to identify factors affecting learners' satisfaction with MOOCs

Massive Online Open Courses (MOOCs) offer free access to training in various topics in all fields. However, the low percentage of course completion by learners is a significant challenge for these platforms. Previous studies on this challenge have investigated user behavior and concerned topics in discussion forums, but these data are mostly momentary and cannot be used for long-term improvement. Thus, this study aimed to address this gap by analyzing learners' comments to identify the factors affecting user satisfaction and prioritize them to improve MOOC platforms. The purpose was to analyze the feedback and actual experiences of users shared through their comments on MOOC online platforms to explore factors affecting user satisfaction to optimize MOOC platforms. To achieve this, sentiment analysis and topic modeling techniques were applied to the user feedback on courses with popular topics, such as Skills for Data Science Teams and Data-Driven Decision Making, available on Coursera.com. The study used DEMATEL analysis, which uses a relation matrix of factors to rank them based on their interrelationships, and network analysis to prioritize the factors that should be improved to achieve the highest user satisfaction. The effect of the proposed approach was investigated through a case study on a course from Coursera. The findings demonstrate that the suggested method has the potential to assist MOOC platforms in several ways. Firstly, it enables the identification of course strengths and weaknesses. Secondly, it allows for the identification of factors that influence learner satisfaction by analyzing user feedback. Lastly, it aids in prioritizing the factors that should be enhanced to attain optimal user satisfaction, thus leading to overall improvement in the status of the MOOC platform.

유튜브 영어 학습 콘텐츠의 진행자 요인에 따른 이용자 인식

This study explored how users' perceptions and responses to YouTube English learning content vary depending on whether the host is a native or non-native English speaker. To this end, two YouTube channels, one hosted by a native English speaker and the other by a non-native English speaker, were selected, and user comments on the learning content were analyzed using the topic modeling technique. The findings revealed that users of both channels perceived the use of the content as a form of 'learning', studying English expressions, vocabulary, and pronunciation on their own as well as asking questions and checking their understanding through comments. On the other hand, there were clear differences in the comments of the two channels according to the host factor. In the contents of the native English speaker, more comments were revealed that users were aware of the host as a native English speaker rather than a mere content creator. These comments encompassed discussions on authentic English expressions and topics tied to American culture. Conversely, comments on the non-native English speaker’s contents revealed a positive evaluation of the host's personal capabilities and learning strategies. These findings indicated the variability of users' perceptions, reactions, and expectations based on the host, irrespective of similarities in content and delivery format. The results also emphasize the potential of YouTube as a platform for English learning and the significance of host-related factors in shaping user reactions to the content.

Exploring Information Technology Industry Programming Language Trends with Non-Negative Matrix Factorization Topic Modelling

The study aimed to determine the preferred programming language among information technology professionals using non-negative matrix factorization topic modelling technique. The results showed that the majority of participants were software developers and programmers who commonly used C#, Java, and Python, and agreed that Java, C, or C++ would be the best language to start with when learning programming. The application of topic modelling revealed key themes such as online video tutorials as effective learning resources, hands-on activities as key to learning programming, importance of feedback, effective teaching strategies, and problem-solving skills as crucial for success in programming. Results of the study may serve as baseline data for the improvement of curricular offerings.

Mapping Metaverse Research: Identifying Future Research Areas Based on Bibliometric and Topic Modeling Techniques

The metaverse represents an immersive digital environment that has garnered significant attention as a result of its potential to revolutionize various industry sectors and its profound societal impact. While academic interest in the metaverse has surged, a dearth of comprehensive review articles employing bibliometric techniques remains. This study seeks to address this gap by analyzing 595 metaverse-related journal articles using bibliometric and topic modeling techniques, marking the first of its kind to investigate the bibliometric profile of metaverse research. The findings reveal exponential growth in metaverse research since 2020, identifying major trends, prolific authors, and the most active journals in the field. A keyword co-occurrence analysis further uncovers four significant clusters of metaverse-related interests, highlighting its unique facets and underscoring its far-reaching implications across various sectors, including education, healthcare, retail, and tourism. This study emphasizes the need for more research and collaboration in advancing the metaverse field and presents 27 research questions for future investigation. This comprehensive analysis serves as a foundation for understanding the current state of metaverse research and its potential trajectory.

Automated issue assignment using topic modelling on Jira issue tracking data

AbstractAs more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.

Incorporating structural topic modeling into short text analysis

Abstract The past few decades have seen the rapid development of topic modeling. So far, research has been more concerned with determining the ideal number of topics or meaningful topic clustering words than with applying topic modeling techniques to evaluate linguistic theories. This study proposes the Structural Topic Model (STM)-led framework to facilitate the interpretation of topic modeling results and standardize text analysis. STM encompasses various model training mechanisms, thereby requiring systematic designs to properly combine language studies. “Structural” in STM refers to the inclusion of metadata structure. Unlike the corpus-based keyness approach, STM can capture contextual cues and meta-information for the interpretation of topical results. Besides, STM can make cross-corpora comparisons via topical contrast, a challenging task for corpus-driven related models such as the Biterm Topic Model (BTM). Stylistic variations in song lyrics are taken as an illustration to show how to use the suggested framework to delve into the linguistic theory proposed by Pennebaker (2013). The topical model and iterable model in the proposed paradigm can clarify how pronouns affect style distinction. We believe the proposed STM-led framework can shed light on text analysis by conducting a reproducible cross-corpora comparison on short texts.

Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps

PurposeThe motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings. Thus, these causes lead to the dissatisfaction of customers. Additionally, these insights reflect the overall rating of the app and it is a source of information to the executive management to contemplate on their services and take timely and effective decisions to improve their mobile app.Design/methodology/approachThis research was conducted on ten reputed Sri Lankan mobile banking apps to analyze the textual opinions of the customers. Data were collected from the Google Play Store considering the higher Android consumers in Sri Lanka. Each review was automatically classified into a relevant sentiment (positive, negative or neutral). These classified reviews were examined along with its rating to identify any discrepancies. The trends of the positive and negative reviews of each app were observed separately along with time. Topic modeling techniques were used to identify the causes of such behavior.FindingsAlthough banks expect to perpetuate good customer reviews all the time, there were aberrant negative trends observed during certain time ranges. The results revealed that unstable versions after recent updates, bad customer service, erroneous functional and nonfunctional features are the root causes toward the dissatisfaction of the customers.Originality/valueNo previous study has been done on the textual reviews of Sri Lankan mobile banking apps. Most studies had considered analyzing the reviews of the app on the entire period of its usage, whereas this research finds the trends where negative reviews surpass the positive reviews and analyze the causes of such behavior.

Examining the Public Messaging on 'Loneliness' over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade.

Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as Twitter and Reddit as well as online forums, e.g., loneliness forums, to seek advice and solace regarding their health and well-being. The present study therefore aimed to investigate the public messaging on loneliness via an unsupervised machine learning analysis of posts made by organisations on Twitter. We specifically examined tweets put out by organisations (companies, agencies or common interest groups) as the public may view them as more credible information as opposed to individual opinions. A total of 68,345 unique tweets in English were posted by organisations on Twitter from 1 January 2012 to 1 September 2022. These tweets were extracted and analysed using unsupervised machine learning approaches. BERTopic, a topic modelling technique that leverages state-of-the-art natural language processing, was applied to generate interpretable topics around the public messaging of loneliness and highlight the key words in the topic descriptions. The topics and topic labels were then reviewed independently by all study investigators for thematic analysis. Four key themes were uncovered, namely, the experience of loneliness, people who experience loneliness, what exacerbates loneliness and what could alleviate loneliness. Notably, a significant proportion of the tweets centred on the impact of the COVID-19 pandemic on loneliness. While current online interactions are largely descriptive of the complex and multifaceted problem of loneliness, more targeted prosocial messaging appears to be lacking to combat the causes of loneliness brought up in public messaging.