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There are examples of how unconscious bias can influence actions of people. In the judiciary, however, despite some examples there is no general theory on whether different demographic attributes such as gender, seniority or ethnicity affect case sentencing. We aim to gain insight into this issue by analyzing over 100k decisions of three different areas of law with the goal of understanding whether judge identity or judge attributes such as gender and seniority can be inferred from decision documents. We find that stylistic features of decisions are predictive of judge identities, their gender and their seniority, a finding that is aligned with results from analysis of written texts outside the judiciary. Surprisingly, we find that features based on legislation cited are also predictive of judge identities and attributes. While own content reuse by judges can explain our ability to predict judge identities, no specific reduced set of features can explain the differences we find in the legislation cited of decisions when we group judges by gender or seniority. Our findings open the door for further research on how these differences translate into how judges apply the law and, ultimately, to promote a more transparent and fair judiciary system. [ABSTRACT FROM AUTHOR]

Classical Chinese poetry has been increasingly popular in recent years, and modeling its topic is quite a promising area of research. Chinese poems have the characteristic of short in length, but traditional topic models perform poorly when faced with short texts due to the text sparsity. Therefore, topic model should be improved to satisfy the scenario of classical Chinese poems. In this paper, a relational background knowledge boosting based topic model (RBKBTM) was proposed to overcome the text sparsity of Chinese poems. We incorporated background information into the model, which expanded the text content from the semantic perspective. The background knowledge was combined using word embedding and TextRank and was then fed into the core computing process. Subsequently, a new sampling formula was derived. Our proposed model was tested on three different tasks using three different datasets. The results demonstrate that the incorporated background knowledge can effectively overcomes text sparsity, improving the performance and effectiveness of the topic model. [ABSTRACT FROM AUTHOR]

In this paper, we describe the supervised dynamic correlated topic model (sDCTM) for classifying categorical time series. This model extends the correlated topic model used for analyzing textual documents to a supervised framework that features dynamic modeling of latent topics. sDCTM treats each time series as a document and each categorical value in the time series as a word in the document. We assume that the observed time series is generated by an underlying latent stochastic process. We develop a state-space framework to model the dynamic evolution of the latent process, i.e., the hidden thematic structure of the time series. Our model provides a Bayesian supervised learning (classification) framework using a variational Kalman filter EM algorithm. The E-step and M-step, respectively, approximate the posterior distribution of the latent variables and estimate the model parameters. The fitted model is then used for the classification of new time series and for information retrieval that is useful for practitioners. We assess our method using simulated data. As an illustration to real data, we apply our method to promoter sequence identification data to classify E. coli DNA sub-sequences by uncovering hidden patterns or motifs that can serve as markers for promoter presence. [ABSTRACT FROM AUTHOR]

This research proposes a framework to improve the efficiency of classification and matching of descriptions of skill on resumes with jobs vacancies using labor market intelligence over a dataset of resumes harvested from social networks. To carry out the experiments, a Kaggle dataset was downloaded containing information from the LinkedIn social network with more than 200,000 records that were later filtered and pre-processed to generate a topic model to classify the entire dataset. Later, using machine learning algorithms, prediction exercises were performed to determine the most efficient match. This model offers high percentages of efficiency when predicting the job position of a candidate of information technology (IT) areas This prediction is achieved due the reduction of categories in these areas generated by the creation of the corresponding topic model to match the resume with the job position. [ABSTRACT FROM AUTHOR]

Abstract There are examples of how unconscious bias can influence actions of people. In the judiciary, however, despite some examples there is no general theory on whether different demographic attributes such as gender, seniority or ethnicity affect case sentencing. We aim to gain insight into this issue by analyzing over 100k decisions of three different areas of law with the goal of understanding whether judge identity or judge attributes such as gender and seniority can be inferred from decision documents. We find that stylistic features of decisions are predictive of judge identities, their gender and their seniority, a finding that is aligned with results from analysis of written texts outside the judiciary. Surprisingly, we find that features based on legislation cited are also predictive of judge identities and attributes. While own content reuse by judges can explain our ability to predict judge identities, no specific reduced set of features can explain the differences we find in the legislation cited of decisions when we group judges by gender or seniority. Our findings open the door for further research on how these differences translate into how judges apply the law and, ultimately, to promote a more transparent and fair judiciary system.

Abstract The Internet of Things (IoT) devices spawn growing diverse social platforms and online data at the network edge, propelling the development of cross-platform applications. To integrate cross-platform data, user identity linkage is envisioned as a promising technique by detecting whether different accounts from multiple social networks belong to the same identity. The profile and social relationship information of IoT users may be inconsistent, which deteriorates the reliability of the effectiveness of identity linkage. To this end, we propose a topic and knowledge-enhanced model for edge-enabled IoT user identity linkage across social networks, named TKM, which conducts feature representation of user generated contents from both post-level and account-level for identity linkage. Specifically, a topic-enhanced method is designed to extract features at the post-level. Meanwhile, we develop an external knowledge-based Siamese neural network for user-generated content alignment at the account-level. Finally, we show the superiority of TKM over existing methods on two real-world datasets. The results demonstrate the improvement in prediction and retrieval performance achieved by utilizing both post-level and account-level representation for identity linkage across social networks.

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website maoyan.www. and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices spawn growing diverse social platforms and online data at the network edge, propelling the development of cross-platform applications. To integrate cross-platform data, user identity linkage is envisioned as a promising technique by detecting whether different accounts from multiple social networks belong to the same identity. The profile and social relationship information of IoT users may be inconsistent, which deteriorates the reliability of the effectiveness of identity linkage. To this end, we propose a topic and knowledge-enhanced model for edge-enabled IoT user identity linkage across social networks, named TKM, which conducts feature representation of user generated contents from both post-level and account-level for identity linkage. Specifically, a topic-enhanced method is designed to extract features at the post-level. Meanwhile, we develop an external knowledge-based Siamese neural network for user-generated content alignment at the account-level. Finally, we show the superiority of TKM over existing methods on two real-world datasets. The results demonstrate the improvement in prediction and retrieval performance achieved by utilizing both post-level and account-level representation for identity linkage across social networks. [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]

Purpose: T his study aims t o provide insight into t he c ontemporary food w aste i ssue f or t he b etter management of food waste in large quantities in Incheon, one specific region in Korea, based on the analysis of the Naver Blog corpus, employing analysis techniques including text mining and LDA topic modeling. Design/methodology/approach: In order to achieve the aforementioned objectives, the current study has employed the R program for the analysis (e.g., TF, tf-idf, correlation, and LDA) of 868 Naver Blog posts which included information about food waste, and/or garbage produced in bulk by foodservice operators in the context of Incheon. Findings: The frequently addressed keywords in the dataset include food, waste, garbage, processor, workplace, food waste dewaterer, microorganism, pulverizer, plastic, compressor, restaurant, sink, cafeteria, and bean sprouts. The correlation analysis demonstrated that waste is largely generated by food material, and food material is closely related to specific types of private companies (e.g., cafeterias) and public places (e.g., military bases, prisons, hospitals). The LDA identified three topics: the implications for food waste produced by the workplace, recent equipment and technologies used for food waste processing, and effect of waste on the environment and call for remedies. Research limitations/implications: While this study has shed light on contemporary issues in relation to food waste in Incheon, it is suggested that the involved parties in the waste management industry pay more attention to the development of effective waste management strategies by hospitality operators in a specific region. Originality/value: This study responds to a lack of understanding underpinning foodservice operators who produce a large quantity of food waste in Incheon, albeit much attention has been paid to some recent research on food waste.

This study addresses the need for a comprehensive understanding of evolving research trends and challenges in Alzheimer’s disease research literature from 2016 to 2023. Employing an improved Dynamic Topic Model (DTM), we analyze the landscape from four critical perspectives: identifying predominant topics, analyzing variability in topic intensity, tracing evolutionary trajectories, and delineating development patterns of key research terms. Through an intensive investigation of four pivotal topics, we emphasize the imperative for sustained attention to these areas, crucial for guiding future research initiatives. Our findings reveal notable fluctuations in topic intensity, primarily attributed to nascent research domains lacking well-defined directions and cohesive research teams. Moreover, we observe a tendency for topics of high similarity to converge over time, signifying maturation and consolidation within the field. Importantly, our study underscores how focal points in Alzheimer’s disease research shift across developmental stages, shaped by dynamic interactions among the research community’s social dynamics, technological advancements, and evolving scientific priorities.

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]

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]

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]

In this study, we conducted an in-depth analysis spanning 53 years, from 1969 to 2021, focusing on the field of literacy studies within the context of libraries. Our exploration involved a dataset of 4,986 articles retrieved from the Scopus database. Our primary objective was to visualize knowledge by identifying and exploring prominent trends and hotspots in literacy studies. To achieve this, we adopted a comprehensive approach. The methodology employed in this study combined traditional approaches with contemporary tools. The dataset was analyzed using the R software for conventional methodologies, while MATLAB was utilized for cutting-edge techniques. The multifaceted approach allowed us to uncover patterns of continuous growth, identify key contributors, and employ the Latent Dirichlet Allocation (LDA) model to recognize emerging and significant topics. The study revealed a consistent pattern of continuous growth in the field of literacy studies, indicating the acquisition of new knowledge over time. Key contributors, including productive authors, influential journals, and active countries, were identified. The application of the LDA model enabled us to recognize newly emerged, developed, and important topics. The significance of this research lies in its contribution to understanding the dynamic landscape of literacy studies within library contexts, offering valuable insights for future research and practical applications in the field. [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]

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.

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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]

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]

Political opposition to fiscal climate policy, such as a carbon tax, typically appeals to fiscal conservative ideology. Here, we ask to what extent public opposition to the carbon tax in Canada is, in fact, ideological in origin. As an object of study, ideology is a latent belief structure over a set of issue topics—and in particular their relationships—as revealed through stated opinions. Ideology is thus amenable to a generative modeling approach within the text-as-data paradigm. We use the Structural Topic Model, which generates word content from a set of latent topics and mixture weights placed on them. We fit the model to open-ended survey responses of Canadians elaborating on their support of or opposition to a carbon tax, then use it to infer the set of mixture weights used by each response. We demonstrate this set, moreso than the observed word use, serves efficient discrimination of opposition from support, with near-perfect accuracy on held-out data. We then operationalize ideology as the empirical distribution of inferred topic mixture weights. We propose and use an evaluation of ideology-driven beliefs based on four statistics of this distribution capturing the specificity, variability, expressivity, and alignment of the underlying ideology. We find that the ideology behind responses from respondents who opposed the carbon tax is more specific and aligned, much less expressive, and of similar variability as compared with those who support the tax. We discuss the implications of our results for climate policy and of broad application of our approach in social science.

IntroductionOver 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 methodsBibliometric 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.ResultsThe 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 and lupin received little attention 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.DiscussionIn 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.

[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 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.

In this paper, we describe the supervised dynamic correlated topic model (sDCTM) for classifying categorical time series. This model extends the correlated topic model used for analyzing textual documents to a supervised framework that features dynamic modeling of latent topics. sDCTM treats each time series as a document and each categorical value in the time series as a word in the document. We assume that the observed time series is generated by an underlying latent stochastic process. We develop a state-space framework to model the dynamic evolution of the latent process, i.e., the hidden thematic structure of the time series. Our model provides a Bayesian supervised learning (classification) framework using a variational Kalman filter EM algorithm. The E-step and M-step, respectively, approximate the posterior distribution of the latent variables and estimate the model parameters. The fitted model is then used for the classification of new time series and for information retrieval that is useful for practitioners. We assess our method using simulated data. As an illustration to real data, we apply our method to promoter sequence identification data to classify E. coli DNA sub-sequences by uncovering hidden patterns or motifs that can serve as markers for promoter presence.

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.

[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]

The safety of air traffic control (ATC) operations is an important cornerstone for the sustainable development of the civil aviation industry. In order to clarify the risk factors in the control operation process and to achieve digital representation of the safety risks of civil aviation control operations, starting from the ATC incident reports, we fully mine the safety risk information and unspoken rules of ATC operations. A risk perception model for air traffic control operations safety based on the Latent Dirichlet Allocation (LDA) topic model and the Semantic Network Based on BERT (BSN) model is suggested. First, 17 risk topics and keywords were found in the incident reports collected using the LDA topic model. These topics included those pertaining to the stage of aircraft operation, human factors in control operation, and the sector or airspace operation status and structure. The findings indicate that while most risk subjects have not changed significantly, they do show an upward tendency. Human factors and operational rules and procedures account for the highest share of all key causes, and they also have a significant impact on how risk topics evolve over time. Finally, the BSN model in the air traffic control field was built based on the keywords of each risk issue in order to highlight any potential correlations between distinct risk topics. The results show that some risk topics have interrelated risk characteristics, and there are regularities of mutual evolution between these risk topics. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks. [ABSTRACT FROM AUTHOR]

Abstract Laws and legal decision-making regulate how societies function. Therefore, they evolve and adapt to new social paradigms and reflect changes in culture and social norms, and are a good proxy for the evolution of socially sensitive issues. Here, we use an information-theoretic methodology to quantitatively track trends and shifts in the evolution of large corpora of judicial decisions, and thus to detect periods in which disruptive topics arise. When applied to a large database containing the full text of over 100,000 judicial decisions from Spanish courts, we are able to identify an abrupt change in housing-related decisions around 2016. Because our information-theoretic approach pinpoints the specific content that drives change, we are also able to interpret the results in terms of the role played by legislative changes, landmark decisions, and the influence of social movements.

In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial role in product development and improvement. However, extracting valuable insights from the vast pool of unstructured text data found in user feedback presents significant challenges. In this paper, we propose an innovative approach to tackle this challenge by combining the Contextualized Topic Model (CTM) and the Masked and Permuted Pre-training for Language Understanding (MPNet) model. Our approach aims to create a more accurate and context-aware topic model that enhances the understanding of user experiences and opinions. To achieve this, we first search for the optimal number of topics, focusing on generating distinguishable, general, and unique topics. Next, we perform hyperparameter optimization to fine-tune the model and maximize coherence metrics. The result is an exceptionally effective model that outperforms established topic modeling methods, including LSI, NMF, LDA, HDP, NeuralLDA, ProdLDA, ETM, and the default CTM, achieving the highest coherence CV score of 0.7091. In this study, the combination of CTM and MPNet has proven highly effective in the context of user feedback topic modeling. This model excels in generating coherent, distinguishable, and highly relevant user feedback topics, capturing the nuanced nature of user feedback data. The topics generated from this model include ‘Music and Audio Streaming,’ ’Application Performance,’ ‘Banking, Financial Services, and Customer Support,’ ’User Experience,’ ‘Other Topics,’ ’Application Content,’ and ‘Application Features.’ Our contributions include a powerful tool for developers to gain deeper insights, prioritize actions, and enhance user satisfaction by incorporating feedback into future product iterations. Furthermore, we introduce a new dataset as an open-source resource for further exploration and validation of user feedback analysis techniques and general natural language processing applications. With our proposed approach, we strive to drive business success, improve user experiences, and inform data-driven decision-making processes, ultimately benefiting both developers and users alike.

Social networks are becoming increasingly popular and significant. One of the most distinctive features of these networks is their dynamic nature, which means that they change over time. Consequently, the community structure on these platforms also changes with time, making the detection of community structure a crucial area of research. Specifically, there is still a lack of understanding of how social networks and communities evolve over time. In this paper, we reveal that individual changing topics (i.e., individual temporal semantics) are a vital factor that drives community evolution. A novel dynamic community detection model is proposed, which takes into account natural evolutionary features. The model first partitions social networks into snapshots. It then detects the community structure at each snapshot by utilizing individual changing topics and information from the previous snapshot. Finally, the evolution of users’ interested topic distributions and topic distributions of communities are identified. The model is compared with five state-of-the-art baselines on two real datasets, and the experimental results demonstrate that our model outperforms all baselines.

For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ’ improvement results in an understanding of the accidents’ nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.

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.

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.

The rapid transition of livestock husbandry in the 20th century involved a broad adoption of slurry-based livestock housing systems that resulted in farm economic benefits, but also in societal debate related to the environment and animal welfare. In this article, we apply the method of topic modeling to four major German newspapers to identify thematic emphases and changes in coverage around "slurry". We considered more than 2300 articles published between 1971 and 2020. Our results show that reporting encompasses economic, environmental, and social topics in which slurry is represented mostly critically ("poisonous substance"), occasionally neutrally ("scent of countryside"), or rarely positively ("input for the bioeconomy"). Three meta-themes overarch the majority of issues and reflect public discourse on agriculture: (i) the dichotomy of agricultural industrialization and family farming; (ii) contrasting actualities of factory farming and animal welfare; and (iii) the responsibility of policy for the emergence, existence and solution of livestock and slurry-related problems. A more balanced recognition of mutual values and constraints by the media could contribute to a discursive reconciliation of public and private interests. [ABSTRACT FROM AUTHOR]

Neural networks, primarily recurrent and convolutional Neural networks, have been proven successful in text classification. However, convolutional models could be limited when classification tasks are determined by long-range semantic dependency. While the recurrent ones can capture long-range dependency, the sequential architecture of which could constrain the training speed. Meanwhile, traditional networks encode the entire document in a single pass, which omits the hierarchical structure of the document. To address the above issues, this study presents T-HMAN, a Topic-aware Hierarchical Multiple Attention Network for text classification. A multi-head self-attention coupled with convolutional filters is developed to capture long-range dependency via integrating the convolution features from each attention head. Meanwhile, T-HMAN combines topic distributions generated by Latent Dirichlet Allocation (LDA) with sentence-level and document-level inputs respectively in a hierarchical architecture. The proposed model surpasses the accuracies of the current state-of-the-art hierarchical models on five publicly accessible datasets. The ablation study demonstrates that the involvement of multiple attention mechanisms brings significant improvement. The current topic distributions are fixed vectors generated by LDA, the topic distributions will be parameterized and updated simultaneously with the model weights in future work. [ABSTRACT FROM AUTHOR]

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility. [ABSTRACT FROM AUTHOR]

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators. [ABSTRACT FROM AUTHOR]

ObjectiveRecently, many tumor sequencing studies have inferred and reported on mutational signatures, short nucleotide patterns at which particular somatic base substitutions appear more often. A number of signatures reflect biological processes in the patient and factors associated with cancer risk. Our goal is to infer mutational signatures appearing in colon cancer, a cancer for which environmental risk factors vary by cancer subtype, and compare the signatures to those in adult stem cells from normal colon. We also compare the mutational signatures to others in the literature.ResultsWe apply a probabilistic mutation signature model to somatic mutations previously reported for six adult normal colon stem cells and 431 colon adenocarcinomas. We infer six mutational signatures in colon cancer, four being specific to tumors with hypermutation. Just two signatures explained the majority of mutations in the small number of normal aging colon samples. All six signatures are independently identified in a series of 295 Chinese colorectal cancers.

Gaining a complete picture of the activity in a city using vast data sources is challenging yet potentially very valuable. One such source of data is Twitter which generates millions of short spatio-temporally localized messages that, as a collection, have information on city regions and many forms of city activity. The quantity of data, however, necessitates summarization in a way that makes consumption by an observer efficient, accurate, and comprehensive. We present a two-step process for analyzing geotagged twitter data within a localized urban environment. The first step involves an efficient form of latent Dirichlet allocation, using an expectation maximization, for topic content summarization of the text information in the tweets. The second step involves spatial and temporal analysis of information within each topic using two complimentary metrics. These proposed metrics characterize the distributional properties of tweets in time and space for all topics. We integrate the second step into a graphical user interface that enables the user to adeptly navigate through the space of hundreds of topics. We present results of a case study of the city of Madrid, Spain, for the year 2011 in which both large-scale protests and elections occurred. Our data analysis methods identify these important events, as well as other classes of more mundane routine activity and their associated locations in Madrid.

The pace of digitized collective knowledge accumulation has become increasingly rapid in the last few years. That means we have tremendous amounts of information content to be organized, searched, and understood that can be arranged only by employing automatic methods. In the case of textual data analysis, topic modeling, a machine learning method, is definitely the most famous framework to uncover latent topics from text documents. Adopting topic modeling approaches for studying textual sources is a well-established practice in many scientific and humanities studies fields, including the historical research scope. In this paper, we present a benchmark corpus for topic models, a dataset containing an annotated real-world collection of texts focused on the antisemitism theme in 19th century France. The benchmark corpus has been developed to address a specific machine learning task but it can also support the enhancement of other natural language processing-based studies, in particular, those concerning the historical sphere.

The purpose of this research was to use develop a sentiment model using machine learning algorithms for discerning public response about the misinformation correction practices of Muslim clerics on YouTube.