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Online review platforms often present reviews from both critics and general users. In this research, the authors propose a measure called "topic consistency" to capture the degree of overlap between critic and user review content. High topic consistency suggests greater information recall due to repeated presentation of the same topics, which may increase the memorability of movie attributes and therefore positively affect movie demand. The authors measure the topic consistency between critic and user reviews using topic models and further study the financial consequences of this measure using data for movies released in the United States. Topic consistency is positively associated with subsequent box office revenue, suggesting a positive relationship between topic consistency and movie demand. Furthermore, the effect of topic consistency on demand is the greatest for movies with mediocre review ratings and when the review ratings from critics are close to those from users. Using lab experiments, the authors provide evidence of the causal link between topic consistency and consumers' willingness to watch a movie, and support for the potential mediation through the information recall of reviews. Movie producers and advertisers should consider highlighting or inducing a central theme for critics and users to discuss, as the more the review content of critics and users overlaps, the higher a movie's revenue. [ABSTRACT FROM AUTHOR]

We apply a dynamic influence model to the opinions of the US federal courts to examine the role of the US Supreme Court in influencing the direction of legal discourse in the federal courts. We propose two mechanisms for how the Court affects innovation in legal language: a selection mechanism where the Court's influence primarily derives from its discretionary jurisdiction, and an authorship mechanism in which the Court's influence derives directly from its own innovations. To test these alternative hypotheses, we develop a novel influence measure based on a dynamic topic model that separates the Court's own language innovations from those of the lower courts. Applying this measure to the US federal courts, we find that the Supreme Court primarily exercises influence through the selection mechanism, with modest additional influence attributable to the authorship mechanism. This article is part of the theme issue 'A complexity science approach to law and governance'. [ABSTRACT FROM AUTHOR]

As one of the oldest continuously publishing journals in statistics (published since 1901), Biometrika provides a unique window onto the history of statistics and its epistemic development throughout the 20th and the beginning of the 21st centuries. While the early history of the discipline, with the works of key figures, such as Karl Pearson, Francis Galton, or Ronald Fisher, is relatively well known, the later (and longer) episodes of its intellectual development remain understudied. By applying digital tools to the full-text corpus of the journal articles (N  =   5,596), the objective of this study is to provide a novel quantitative exploration of the history of the statistical sciences via an all-encompassing view of 120 years of Biometrika. To this aim, topic-modelling analyses are used and provide insights into the epistemic content of the journal and its evolution. Striking changes in the thematic content of the journal are documented and quantified for the first time, from the decline of Pearsonian and Weldonian biometrical research and the journal's tight connection to biology in the 1930s to the rise of modern statistical methods beginning in the 1960s and 1970s. Newly developed approaches are used to infer author networks from publication topics. The resulting network of authors shows the existence of several communities, well-aligned with topic clusters and their evolution through time. It also highlights the role of specific figures over more than a century of publishing history and provides a first window onto the foundation, development, and diverse applications of the statistical sciences. [ABSTRACT FROM AUTHOR]

Between 2015 and 2017, the Internet Research Agency (IRA) – a Kremlin-backed "troll farm" based in St. Petersburg – executed a propaganda campaign on Twitter to target US voters. Scholarship has expended relatively little effort to study the role of Islamophobia in the IRA's propaganda campaign. Following critical disinformation research, this article demonstrates that Islamophobia, affect, and white identity played a crucial role in the IRA's targeting of right-wing US voters. With an official release of tweets and associated visual content from Twitter, we use topic modeling and visual analysis to explore both how, and to what extent, the IRA used Islamophobia in its propaganda. To do so, we develop a multimodal distant reading technique to study how the IRA aligned users with contemporary far right social movements by deploying racial and emotional appeals that center on narrating a transnational white identity under threat from Islam and Muslims. [ABSTRACT FROM AUTHOR]

To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which fuses LDA-based topic features and BERT-based semantic features, as well as using contrastive learning, to enhance the disambiguation process. Experiments on a publicly available Chinese short-text entity disambiguation dataset show that the proposed model achieves an F1-score of 84.0%, which outperforms the benchmark method by 0.6%. Moreover, our model achieves an F1-score of 74.5% with a limited number of training samples, which is 2.8% higher than the benchmark method. These results demonstrate that our model achieves better effectiveness and robustness and can reduce the burden of data annotation as well as training costs. [ABSTRACT FROM AUTHOR]

As an extension to the standard symmetric latent Dirichlet allocation topic model, we implement asymmetric Beta-Liouville as a conjugate prior to the multinomial and therefore propose the maximum a posteriori for latent Beta-Liouville allocation as an alternative to maximum likelihood estimator for models such as probabilistic latent semantic indexing, unigrams, and mixture of unigrams. Since most Bayesian posteriors, for complex models, are intractable in general, we propose a point estimate (the mode) that offers a much tractable solution. The maximum a posteriori hypotheses using point estimates are much easier than full Bayesian analysis that integrates over the entire parameter space. We show that the proposed maximum a posteriori reduces the three-level hierarchical latent Beta-Liouville allocation to two-level topic mixture as we marginalize out the latent variables. In each document, the maximum a posteriori provides a soft assignment and constructs dense expectation–maximization probabilities over each word (responsibilities) for accurate estimates. For simplicity, we present a stochastic at word-level online expectation–maximization algorithm as an optimization method for maximum a posteriori latent Beta-Liouville allocation estimation whose unnormalized reparameterization is equivalent to a stochastic collapsed variational Bayes. This implicit connection between the collapsed space and expectation–maximization-based maximum a posteriori latent Beta-Liouville allocation shows its flexibility and helps in providing alternative to model selection. We characterize efficiency in the proposed approach for its ability to simultaneously stream both large-scale data and parameters seamlessly. The performance of the model using predictive perplexities as evaluation method shows the robustness of the proposed technique with text document datasets. [ABSTRACT FROM AUTHOR]

The scholarly literature at the intersection of sales and ethics is vast and, therefore, difficult to summarize. To explore the state of the sales–ethics landscape, the authors apply probabilistic topic modeling to a dataset composed of 293 journal articles published from 1980 to 2022. The critical examination of the results leads to a framework that identifies 32 topics and groups these topics into five high-level topic areas. Building on these topics and topic areas, the authors explore where future research on sales and ethics should focus, using in-depth interviews with 30 scholars and 15 practitioners. The results of these interviews reveal important implications for sales and ethics and overarching research questions regarding (1) understanding existing realities, (2) understanding new realities, and (3) advancing research practices. In doing so, this study provides a platform for much-needed research and scholarly discourse on sales and ethics. [ABSTRACT FROM AUTHOR]

Unlike text analysis for which topic models are historically developed, traffic video analysis is dealing with much simpler topics, made of restricted motion patterns. In this paper, we propose a dual-layer direction-oriented framework for more efficient traffic motion patterns description based on topic models through considering the simplicity of traffic topics. The aforesaid framework compels the involved topic models to learn the foreknown visually meaningful motion patterns that exist in traffic scenes, as developed theoretically in this paper. Experimental results produced by common datasets show that the proposed method provides more intuitive topics for traffic flow description. Based on experimental results, our framework outperforms other topic-model based methods by 4% to more than 11% in detecting abnormal events, in terms of the area under the Receiver Operating Characteristic curve. In addition to that, in a scene analysis evaluation at intersections equipped with traffic signals, our method reaches 4% higher traffic phase detection accuracy, compared to conventional topic models. [ABSTRACT FROM AUTHOR]

Purpose: It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side. Design/methodology/approach: To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention. Findings: The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams. Practical implications: The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies. Originality/value: This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented. [ABSTRACT FROM AUTHOR]

Background: Autonomous ships have the potential to increase operational efficiency and reduce carbon footprints through technology and innovation. However, there is no comprehensive literature review of all the different types of papers related to autonomous ships, especially with regard to their integration with ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. Methods: A machine learning method is used to extract the main topics from more than 2000 journal publications indexed in WoS and Scopus. Results: The research findings highlight key issues related to technology, cybersecurity, data governance, regulations, and legal frameworks, providing a different perspective compared to human manual reviews of papers. Conclusions: Our search results confirm several recommendations. First, from a technological perspective, it is advised to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate testing of wave model evaluation systems, and promote international standardization. Second, from a cyber–physical systems perspective, efforts should be made to strengthen logistics and supply chains for autonomous ships, establish data governance protocols, enforce strict control over IoT device data, and strengthen cybersecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can be achieved by promoting international agreements from a global societal standpoint and clarifying the legal framework regarding liability in the event of accidents. [ABSTRACT FROM AUTHOR]

Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development.

Prior empirical efforts in uncovering the research-practice gap in public relations have often been restricted to perceptions and evaluations of people participating in the investigation. Moving beyond the linear perspective on knowledge transfer that dominates relevant discussions for decades, this study adopted topic modeling as an inductive analytical approach to examine a comprehensive set of texts representing the perspective of scholars and practitioners over a 10-year period from 2011 to 2020. A comparison of 35 topics discerned from academic journals (1,209 titles/abstracts) and professional texts (2,378 articles) revealed that a total of 18 topics were peculiar to each corpus, providing sound evidence of the substantial divide between scholars and practitioners. However, two communities shared common or comparable concerns over 17 topics, suggesting a significant convergence on crucial issues. Moreover, scholars and practitioners assigned varying weights to these topics in their publications, which indicated noteworthy differences in the primary areas of interest for both communities. In addition to deepening our understanding of the width and nuances of the research-practice gap in the field of public relations in a quantitative way, findings obtained from this study also signal the direction toward which scholars and practitioners should make progress to bridge the gap. [ABSTRACT FROM AUTHOR]

Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm. [ABSTRACT FROM AUTHOR]

We review popular unsupervised learning methods for the analysis of high‐dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K‐means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low‐rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data. [ABSTRACT FROM AUTHOR]

With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress. [ABSTRACT FROM AUTHOR]

Urbanization is crucially important for people to improve the quality of life. Thus, it is of importance to study the evolution of hot topics to explore the functions of cities for meeting the increasing demands of people. In this paper, we explored the semantic analysis of hot topics and trends in urban studies from the literature, which provides a research direction for future studies. Based on articles collected from the Science Citation Index Expanded and Conference Proceedings Citation Index-Science databases from 2000 to 2016, we found that the number of urban studies increased in stability during that time. Followed by England and China, USA was the largest contributor for studies in this field. Based on the keywords and abstracts of these articles, we extracted the topics of the study using a clustering method and topic model, and calculated the hot values of the topics. Finally, we obtained 15 hot topics in the field of urban studies, among which "city", "school", "regional economic", and "estate" were the hottest topics that indicated the focus of the research study. An anomaly detection method was used to analyze the change trend of topics' hot values, and we found that the hot value of these topics overall were on the rise, especially "urban education" and "urban planning" increased significantly, which indicated that they attracted an increasing amount of scholars' attention, but the hot value of "health" and "Gis" decreased significantly recently, which suggested that research interest in these two topics is decreasing. [ABSTRACT FROM AUTHOR]

Introduction: Over the last decade, there has been a growing interest in cereal-legume intercropping for sustainable agriculture. As a result numerous papers, including reviews, focus on this topic. Screening this large amount of papers, to identify knowledge gaps and future research opportunities, manually, would be a complex and time consuming task. Materials and methods: Bibliometric analysis combined with text mining and topic modelling, to automatically find topics and to derive a representation of intercropping papers as a potential solution to reduce the workload was tested. Both common (e.g. wheat and soybean) as well as underutilized crops (e.g. buckwheat, lupin, triticale) were the focus of this study. The corpus used for the analysis was retrieved from Web of Science and Scopus on 5th September 2022 and consisted of 4,732 papers. Results: The number of papers on cereal-legume intercropping increased in recent years, with most studies being located in China. Literature mainly dealt with the cereals maize and wheat and the legume soybean whereas buckwheat andlupinreceivedlittleattention from academic researchers. These underutilized crops are certainly interesting to be used as intercropping partners, however, additional research on optimization of management and cultivar's choice is important. Yield and nitrogen fixation are the most commonly studied traits in cereal-legume intercropping. Last decade, there is an increasing interest in climate resilience, sustainability and biodiversity. Also the term "ecosystem services" came into play, but still with a low frequency. The regulating services and provisioning services seem to be the most studied, in contrast terms related to potential cultural services were not encountered. Discussion: In conclusion, based on this review several research opportunities were identified. Minor crops like lupin and buckwheat need to be evaluated for their role as intercropping partners. The interaction between species based on e.g. root exudates needs to be further unraveled. Also diseases, pests and weeds in relation to intercropping deserve more attention and finally more in-depth research on the additional benefits/ecosystem services associated with intercropping systems is necessary. [ABSTRACT FROM AUTHOR]

As the main driving force for the digital transformation of the construction industry, the uncertainty of digital technology in the application process has seriously hindered the high-quality development of the construction industry. In order to promote the wide application of digital technology in the construction industry and clarify the key uncertainties in its application process, this paper identifies the uncertainty index system of digital technology application based on the LDA topic model and literature analysis; the DEMATEL-ISM method is used to construct the multilevel hierarchical structure model of the uncertainty indicators in the application of digital technology to study the mutual influence among the indicators and to find the key uncertainty indicators. The research results show that the uncertainty indicators of the application of digital technology in the construction industry are divided into five levels: policy, industry, personnel, economy and law, and that the perfection of the policy guarantee system is the key uncertainty indicator for the investment return period of digital technology application. The standard contract model for digital technology is a direct uncertainty indicator for the application of digital technology in the construction industry. The results of this study help researchers and practitioners to focus on the key barriers and provide a list of key elements for construction companies to promote the application of next-generation digital technologies to improve digitalization of the construction industry. This study also provides a policy reference to further promote the digital transformation of the construction industry. [ABSTRACT FROM AUTHOR]

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Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models. [ABSTRACT FROM AUTHOR]

Purpose: The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives. Design/methodology/approach: The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics. Findings: Different types of vehicle consumers have the same demands, such as "Space," "Power Performance," and "Brand Comparison," and distinct demands, such as "Appearance," "Safety," "Service," and "New Energy Features"; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process. Research limitations/implications: The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further. Practical implications: First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing. Originality/value: The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share. [ABSTRACT FROM AUTHOR]

Abstract Background Topic models are a class of unsupervised machine learning models, which facilitate summarization, browsing and retrieval from large unstructured document collections. This study reviews several methods for assessing the quality of unsupervised topic models estimated using non-negative matrix factorization. Techniques for topic model validation have been developed across disparate fields. We synthesize this literature, discuss the advantages and disadvantages of different techniques for topic model validation, and illustrate their usefulness for guiding model selection on a large clinical text corpus. Design, setting and data Using a retrospective cohort design, we curated a text corpus containing 382,666 clinical notes collected between 01/01/2017 through 12/31/2020 from primary care electronic medical records in Toronto Canada. Methods Several topic model quality metrics have been proposed to assess different aspects of model fit. We explored the following metrics: reconstruction error, topic coherence, rank biased overlap, Kendall’s weighted tau, partition coefficient, partition entropy and the Xie-Beni statistic. Depending on context, cross-validation and/or bootstrap stability analysis were used to estimate these metrics on our corpus. Results Cross-validated reconstruction error favored large topic models (K ≥ 100 topics) on our corpus. Stability analysis using topic coherence and the Xie-Beni statistic also favored large models (K = 100 topics). Rank biased overlap and Kendall’s weighted tau favored small models (K = 5 topics). Few model evaluation metrics suggested mid-sized topic models (25 ≤ K ≤ 75) as being optimal. However, human judgement suggested that mid-sized topic models produced expressive low-dimensional summarizations of the corpus. Conclusions Topic model quality indices are transparent quantitative tools for guiding model selection and evaluation. Our empirical illustration demonstrated that different topic model quality indices favor models of different complexity; and may not select models aligning with human judgment. This suggests that different metrics capture different aspects of model goodness of fit. A combination of topic model quality indices, coupled with human validation, may be useful in appraising unsupervised topic models.

Schools develop mission statements in part to communicate their purposes of schooling to internal and external audiences. The goal of this study is to employ text analytic techniques to analyze school mission statements. Focusing on Arizona and New Jersey's schools, we seek to understand: 1) what themes (topics) emerge from their mission statements, 2) whether local context (politics) and institutional factors (being a charter) mediate topic prevalence, and 3) how similar school mission statements are to one another overall and across select factors. Using structural topic modeling we find that five topics emerge as particularly prevalent, and they address key purposes within the US context related to social, academic, and civic development with an emphasis on a safe environment and a sense of community. Local political leanings have a minor impact on the prevalence of topics but being a charter has a distinct influence. Compared to regular schools, charters are more likely to include themes related to preparing for college, academic approaches, and character and family ties, but they place lower emphasis on community and citizenship themes. School missions display moderate levels of similarity, indicating that a common set vocabulary has permeated schools' missions, though differences remain, including between states. Overall, this study deepens our understanding of the varieties of schools' missions and reveals text analytic techniques are well suited for uncovering them. Plain Language Summary: Schools develop mission statements in part to communicate their purposes of schooling to internal and external audiences. In a novel application, the goal of this study is to employ text analytic techniques to analyze school mission statements. Focusing on Arizona and New Jersey schools, we seek to understand what themes (topics) emerge from their mission statements, whether local context (politics) and institutional factors (being a charter) mediate topic prevalence, and how similar school mission statements are to one another overall and across select factors. Employing structural topic modeling, five themes emerge as most prevalent. The themes broadly address key purposes within the US context related to social, academic, and civic development with an emphasis on a safe environment and a sense of community. Local political leanings have a minor impact on the prevalence of topics but being a charter has a distinct influence. Compared to regular schools, charters are more likely to include themes related to preparing for college, academic approaches, and character and family ties, but they place lower emphasis on community and citizenship themes. School missions display moderate levels of similarity, indicating that a common set vocabulary has permeated, though differences remain, including between states. One limitation of the study is the focus on two states; however, preliminary analyses of a small national sample suggest comparable vocabularies. Overall, this study reveals school mission statements present meaningful variations in the purposes of schools. Schools are communicating interpretable goals and text data mining techniques are well suited for uncovering them. [ABSTRACT FROM AUTHOR]

The article focuses on addressing a critical limitation in recommender systems based on knowledge graphs (KGs) by introducing a new model called Topic-aware Intention Network (TIN) to explicitly model user intent for improved recommendations. Topics covered include the shortcomings of existing KG-based recommender systems, the proposed TIN model's architecture, and the benefits of incorporating user intent into recommendations.

Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics |$K$|⁠. Since there is no definitive way to choose |$K$| and since a true value might not exist, we develop a method, which we call topic alignment , to study the relationships across models with different |$K$|⁠. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when |$K$| increases. This strategy gives more insight into the process of generating the data than choosing a single value of |$K$| would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto [ABSTRACT FROM AUTHOR]

[Purpose/Significance] The introduction of national poverty alleviation policies and rural revitalization strategies has thrust the issue of education for left-behind children into the spotlight of scholarly attention. Education, far beyond serving as a mere instrument for personal growth and human capital accumulation for left-behind children, emerges as a pivotal measure in consolidating rural poverty alleviation endeavors and breaking the transmission of intergenerational poverty in China. It stands as a vital force propelling the future of rural revitalization. Yet, the existing literature on the education of left-behind children remains sporadic and dispersed. A more profound organizational effort, integrating, synthesizing, and evaluating this scattered literature, is imperative to establish a foundational framework for future research, fostering more cohesive and focused research endeavors. Presently, literature review studies primarily fall into three categories: qualitative review methods, meta-analysis, and bibliometric analysis methods employing tools like Citespace. This study sets out to achieve a systematic and comprehensive understanding of education-related issues for rural left-behind children through text mining methods grounded in topic models. [Method/Process] The advent of artificial intelligence and machine learning technologies has empowered the processing and analysis of vast amounts of textual data. Previous research, employing latent dirichlet allocation (LDA) topic models, successfully mined texts related to teacher team construction reform policies, internationalization in higher education literature, news reports, and online comments. In this study, a corpus was meticulously constructed using abstract texts extracted from 2037 journal articles published between 2002 and 2023. The structural topic model (STM) was chosen for topic modeling, overcoming the limitations associated with LDA, with a specific emphasis on exploring the diversity and dynamism of topics within the existing literature. [Results/Conclusions] The culmination of this research effort identified eight distinct research themes: psychological well-being, factors leading to left-behind children, macro-level coping strategies, types of guardianship, review studies, family education, media literacy, and micro-level coping strategies. By synergizing document metadata information, the study systematically unraveled the evolving trends of these topics over time, providing crucial insights into potential shifts in the focus of left-behind children's education research. It is essential to note that this study, while collecting abstracts instead of full texts, may not capture the entirety of information contained in complete research articles. Future research endeavors should explore left-behind children's education more comprehensively, leveraging full-text mining techniques for a more nuanced understanding of this critical subject. [ABSTRACT FROM AUTHOR]

Simple Summary: Single-cell RNA sequencing has significantly contributed to the discovery of heterogenous cellular programs that are highly expressed by distinct cell subtypes. Yet, the quantification of cell subtype-specific and shared gene co-expressing modules (GEMs) associated with cell differentiation is a challenge. Herein, we have developed scGEM to uncover such hidden GEMs and conducted a systematic evaluation of model performance as well as a comparison with existing methods. We demonstrate that scGEM has the potential to generate a better biological explanation of GEMs using simulated and real-world datasets. The positive impacts of this study can illuminate the interpretation of gene modules in single-cell transcriptome analysis and shed light on the cell–cell communication and regulatory network. Background: Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types. Methods: In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells. Results: We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset. Conclusions: Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells. [ABSTRACT FROM AUTHOR]

Nowadays, online users write short messages to share their feelings on social networking sites, such as discussion forums, question answering websites, etc., making these sites very popular. The increase in these short-term messages causes a huge data sparsity, making emotion recognition a challenging task. Therefore, a word co-occurrence pattern called biterms is generated from a large-scale dataset to prevent severe data sparsity issues. The topic modeling algorithms and acceleration algorithms are implemented to extract more reliable topics from the group of terms. Based on biterm technique, in this paper, a new algorithm called "Affected biterm emotion topic" is proposed for emotion recognition from a short text. For the experimental purpose, two popular short text datasets, SemEval and International Survey on Emotion Antecedents and Reactions (ISEAR), are used to investigate the performance of the proposed algorithm with the benchmark methods light latent dirichlet allocation (LLDA), biterm topic model (BTM), emotion-topic model (ETM), contextual sentiment topic model (CSTM), Sentiment latent topic model (SLTM) and siasme network based supervised topic model (SNSTM). The proposed algorithm is evaluated using the benchmark methods for mean, variance, and accuracy. The experimental result shows that the proposed algorithm is effective in analyzing emotions. [ABSTRACT FROM AUTHOR]

Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments. [ABSTRACT FROM AUTHOR]

Abstract Existing work generally classifies news headlines as a matter of short text classification. However, due to the strong domain nature and limited text length of news headlines, their classification results are usually determined by several specific keywords, which makes the traditional short text classification method ineffective. In this paper, we propose a new method to identify keywords in news headlines and expand their features from sentence level and word level respectively, and finally use convolutional neural networks (CNN) to extract and classify their features. The proposed model was tested on the Sogou News Corpus dataset and achieved 93.42 $$\%$$ % accuracy.

[Objective] Because the decision-making process of the government follows the principle of “satisfaction” rather than “optimization”, attitude becomes a key element in the decision-making process. From this perspective, this study examined how the ecological governance attitude of higher-level government acts on the attention allocation of lower-level governments. [Methods] Based on the topic model and machine learning techniques, this study examined 3648 provincial and municipal government work reports (2010-2021) sentence by sentence. Then the ecological governance attitude and attention allocation of the provincial and municipal governments were measured and analyzed. [Results] The study found that the ecological governance attitude of higher-level governments can directly and indirectly act on the attention allocation of lower-level governments, and the ecological governance attitude of lower-level governments themselves plays a mediation role in the process. There are differences in the influence of different attitudes on attention. The increase of negative attitude of higher-level governments towards ecological governance significantly improves the attention allocation of lower-level governments, indicating the “deterrent effect” of negative attitude; while the improvement of the positive attitude of higher-level governments reduces the attention allocation of lower-level governments, which indicate the competitive relationship and “crowding out effect” of different issues on local governments’ attention. The results were also verified through robustness tests, including measuring government attention by governance data on the Internet, and testing the “attitude-attention” relationship under the issue of “production safety”. [Conclusion] This article argued that the government should pay attention to the role of government attitude as a management signal, and take the expression framework of government attitude as an important management tool, to reasonably regulate the attention allocation of ecological governance of local governments.

Retelling extraction is an important branch of Natural Language Processing (NLP), and high-quality retelling resources are very helpful to improve the performance of machine translation. However, traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application. In order to solve this problem, we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance. In this method, Probabilistic Latent Semantic Analysis (PLSA) is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition. Then we design a decoder to simplify the decoding process. Experiments show that the proposed method can effectively improve the accuracy of translation.