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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.

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]

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]

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.

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]

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]

Depression raises a double challenge: besides the negative mood and the intrusive thoughts, the relation to the self also becomes difficult. Online forums are analysed as communicative platforms enabling the interactive reconstruction of the self. The discourses of online depression forums are explored. Firstly, narrative patterns are identified according to their thematic focus (e.g. dysfunctional body, challenges of intimacy) and discursive logic (e.g. information exchange, support). Secondly, narratives are analysed in order to describe various ways of grounding a depressed self. ∼70.000 depression-related posts from the biggest English-speaking online forums (e.g. , ) were analysed. Quantitative (LDA topic modelling) and qualitative (deep reading) approaches were used simultaneously to determine the optimal number of topics and their interpretation. 13 topics were identified and interpreted according to their content and communicative function. Based on the inter-topic distances four clusters were identified (medicalized, intimacy-oriented, critical and uninhabitable self-narratives). The clusters of the 13 topics highlight various ways of narrating depression and the depressed self. Based on a comparison with a systematic review of mental illness recovery narratives, depression forums cover most narrative genres and emotional tones, thus create a unique opportunity for integrating the depressing experiences in the self. [ABSTRACT FROM AUTHOR]

Background Owing to the linguistic situation, Japanese natural language processing (NLP) requires morphological analyses for word segmentation using dictionary techniques. Objective We aimed to clarify whether it can be substituted with an open-end discovery-based NLP (OD-NLP), which does not use any dictionary techniques. Methods Clinical texts at the first medical visit were collected for comparison of OD-NLP with word dictionary-based-NLP (WD-NLP). Topics were generated in each document using a topic model, which later corresponded to the respective diseases determined in International Statistical Classification of Diseases and Related Health Problems 10 revision. The prediction accuracy and expressivity of each disease were examined in equivalent number of entities/words after filtration with either term frequency and inverse document frequency (TF-IDF) or dominance value (DMV). Results In documents from 10,520 observed patients, 169,913 entities and 44,758 words were segmented using OD-NLP and WD-NLP, simultaneously. Without filtering, accuracy and recall levels were low, and there was no difference in the harmonic mean of the F-measure between NLPs. However, physicians reported OD-NLP contained more meaningful words than WD-NLP. When datasets were created in an equivalent number of entities/words with TF-IDF, F-measure in OD-NLP was higher than WD-NLP at lower thresholds. When the threshold increased, the number of datasets created decreased, resulting in increased values of F-measure, although the differences disappeared. Two datasets near the maximum threshold showing differences in F-measure were examined whether their topics were associated with diseases. The results showed that more diseases were found in OD-NLP at lower thresholds, indicating that the topics described characteristics of diseases. The superiority remained as much as that of TF-IDF when filtration was changed to DMV. Conclusion The current findings prefer the use of OD-NLP to express characteristics of diseases from Japanese clinical texts and may help in the construction of document summaries and retrieval in clinical settings. [ABSTRACT FROM AUTHOR]

Systematic reviews summarize the progress of studies and pave roads for future research in an academic field. However, conducting a systematic literature review can be burdensome and time-consuming. Computer-assisted methods such as text mining techniques have been increasingly applied to improve systematic reviews in public administration. To test the reliability of using text mining for systematic literature reviews, this study uses clustering, topic modeling, automatic multi-term extraction, and text network to systematically review articles published in Chinese Public Administration Review from 2002 to 2019. By comparing machine-produced topics with existing human-coded themes, findings show that applying text mining methods for systematic reviews can be reliable and effective with cautions. The study also offers practical suggestions for researchers to apply text mining methods for systematic literature reviews. [ABSTRACT FROM AUTHOR]

Information and communication technologies (ICT) have become a key factor in inclusive education by supporting the access and participation of all people in the educational system. Currently, a large number of works have been published on these topics and it takes time to analyze all the information; for this reason, the objective of this work was to discover and identify hidden and/or relevant topics of 717 abstracts corresponding to research on inclusive education and ICT published between 1996 and 2020 through text mining. The modeling of themes found twenty-one topics, and the most representative were those that were related to electronic devices, instructional design, Universal Design for Learning (UDL), and some type of disability or educational need. The three aspects that are key to making inclusive education successful are as follows: the development of policies, the diversification of educational practices using the principles of UDL, and the use of ICT in educational processes. The theme modeling proved to be a useful tool for researchers to analyze large amounts of information and extract the most relevant one. [ABSTRACT FROM AUTHOR]

The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today’s research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.

Investor sentiment has always been an active research topic in finance. In recent years, text mining, machine learning and sentiment analysis have been very fruitful, and researchers can extract valuable information from social platforms more promptly and accurately. Unsupervised learning avoids the subjective human selection of data while reducing the workload. This paper uses the primary model for the unsupervised learning total probability generative model LDA (Latent Dirichlet Allocation). Natural language processing and word-splitting tools empirically analyze text data from a well-known financial and stock information website. An attempt is made to explore the correlation with stock excess return. The significant findings are as follows. First, investor sentiment classified by theme is positively correlated with excess return. Second, different themes have different degrees of influence, with “broad market sentiment” affecting the short term, corporate development involving a long time, and “corporate dividends” affecting both. Third, there is an asymmetric effect of investor sentiment on excess return.

A Topic Model is a class of generative probabilistic models which has gained widespread use in computer science in recent years, especially in the field of text mining and information retrieval. Since it was first proposed, it has received a large amount of attention and general interest among scientists in many research areas. It allows us to discover the mix of hidden or "latent" subjects that differs from one document to another in a given corpus. But since topic modeling usually requires the prior definition of some parameters - above all the number of topics k to be discovered -, model evaluation is decisive to identify an "optimal" set of parameters for the specific data. Latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BerTopic) are the two most popular topic modeling techniques. LDA uses a probabilistic approach whereas BerTopic uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters. [ABSTRACT FROM AUTHOR]

The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of "emerging topics" share the similar characters with the "cover papers", this study present a novel approach to translate the "emerging topics" detection to "cover paper" prediction. By using "AdaBoost model" and topic model, we construct a machine learning framework to imitate the top journal (chief) editor's judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals. [ABSTRACT FROM AUTHOR]

This software review provides a systematic overview of R packages for topic modeling. These packages facilitate the application of computational text analysis to conduct research compatible with a wide variety of methodological frameworks employed in the social and communication sciences. For this overview, the analysis process is divided into four steps: preprocessing, inference, evaluation, and interpretation of topic models. The summary shows that the wide selection of R packages available today provides easy-to-use access to this increasingly popular computational social science method and contributes to its methodical standardization. [ABSTRACT FROM AUTHOR]

The unsafe behavior of construction workers is the key cause of safety accidents. The accident investigation report contains rich experience and lessons, which can be used to prevent and reduce the occurrence of safety accidents. In order to draw lessons from the accident and realize knowledge sharing and reuse, this paper uses text mining technology to analyze the data of 500 construction accident investigation reports in Shenzhen, China. Firstly, a Latent Dirichlet Allocation (LDA) topic model is used to identify the unsafe behavior of construction workers and its influencing factors. Then, with the help of Social Network Analysis, the importance of influencing factors and the relationship between them are identified. The results show that weak safety awareness, operating regulations, supervision dereliction of duty, equipment resources, and inadequate supervision of the construction party are the key and important factors. It is also found that there are correlations between weak safety awareness and supervision dereliction of duty, between equipment resources and poor construction environment, between organization and coordination and inadequate supervision of the construction party, and between operating regulations and hidden dangers investigation. This study not only helps to improve the theoretical system in the field of construction workers' unsafe behavior but also helps managers to find the key control direction of construction safety, so as to effectively curb unsafe behavior of construction workers and improve the level of safety management. [ABSTRACT FROM AUTHOR]

At present, we live in prosperity contrary to the past times. As income increases, people enjoy wealth, but more people tend to pursue their own inner happiness: travel. People go to other places or visit foreign countries for business or journey. This study aims to identify the best tour route for foreign tourists in South Korea. Based on the review analysis results, this paper also aims to put forward techniques and methodologies required in practical affairs when developing a travel site or a travel application. On this note, it collected tourists' reviews at the Tripadvisor official website and conducted text mining technique as well as network analysis using R and Tagxedo, which is a big data analytic tool. The analysis results displayed that there were differences in travel preference, and especially, individual travelers had difficulty traveling by public transportation and selecting travel destinations. Therefore, customized travel routes were suggested for convenient use among travelers. [ABSTRACT FROM AUTHOR]

The unsafe behavior of construction workers is the key cause of safety accidents. The accident investigation report contains rich experience and lessons, which can be used to prevent and reduce the occurrence of safety accidents. In order to draw lessons from the accident and realize knowledge sharing and reuse, this paper uses text mining technology to analyze the data of 500 construction accident investigation reports in Shenzhen, China. Firstly, a Latent Dirichlet Allocation (LDA) topic model is used to identify the unsafe behavior of construction workers and its influencing factors. Then, with the help of Social Network Analysis, the importance of influencing factors and the relationship between them are identified. The results show that weak safety awareness, operating regulations, supervision dereliction of duty, equipment resources, and inadequate supervision of the construction party are the key and important factors. It is also found that there are correlations between weak safety awareness and supervision dereliction of duty, between equipment resources and poor construction environment, between organization and coordination and inadequate supervision of the construction party, and between operating regulations and hidden dangers investigation. This study not only helps to improve the theoretical system in the field of construction workers’ unsafe behavior but also helps managers to find the key control direction of construction safety, so as to effectively curb unsafe behavior of construction workers and improve the level of safety management.

In recent years, with the rapid growth of social media, short texts have been very prevalent on the internet. Due to the limited length of each short text, word co-occurrence information in this type of documents is sparse. Conventional topic models based on word co-occurrence are unable to distill coherent topics on short texts. A state-of-the-art strategy is self-aggregated topic models which implicitly aggregate short texts into latent long documents. But these models have two problems. One problem is that the number of long documents should be defined explicitly and the inappropriate number leads to poor performance. Another problem is that latent long documents may bring non-semantic word co-occurrence which brings incoherent topics. In this article, we firstly apply the Chinese restaurant process to automatically generate the number of long documents according to the scale of short texts. Then to exclude non-semantic word co-occurrence, we propose a novel probabilistic model generating latent long documents in a more semantically way. Specifically, our model employs a pitman-yor process to aggregate short texts into long documents. This stochastic process can guarantee that the distribution between short texts and long documents following a power-law distribution which can be found in social media like Twitter. Finally, we compared our method with several state-of-the-art methods on four real short texts corpus. The experiment results show that our model performs superior to other methods with the metrics of topic coherence and text classification.

Benchmarking is one of the key management approaches to support companies to continuously improve and gain competitive advantage. However, previous benchmarking management suffered from subjectivity and one-sidedness, and the benchmarking evaluation was only to identify the benchmark and its own deficiencies, with a high degree of independence between the sub-processes. The framework is the first to jointly apply topic mining and relation extraction techniques to establish an objective index system and mine the mechanistic relations among the indexes, and finally provides a concise and interactive display of the results based on knowledge graph visualization. In this study, 50 oil and gas companies are taken as examples to carry out the case study of sustainable development benchmarking management, and a sustainable development program with strong logic and systematicity is proposed at both macro and micro levels. This study results verify that the framework can effectively improve the scientific, comprehensive and systematic nature of sustainable development benchmarking management. At the same time, the framework extends the role of the index system so that it can provide the necessary mechanistic guidance for subsequent sustainable development programmatic to achieve programmatic with a high degree of synergy. In addition, the framework has a high degree of coherence with the United Nations Sustainable Development Goals (SDGs), and the proposals put forward can not only be used to improve the sustainable development capability of companies themselves, but also contribute to the realization of the SDGs. [Display omitted] • A novel sustainable development benchmarking framework for energy companies. • A first joint application of topic mining and relation extraction for benchmarking. • Energy companies should promote localization and diversification of the workforce. • Energy companies should expand joint venture projects with developing country. [ABSTRACT FROM AUTHOR]

This paper is a study on an industrial engineering approach for the formulation of digital transformation (DX) strategies in the manufacturing industry. When a company is working on the formulation of a DX strategy, it is not uncommon for the stakeholders in the relevant departments to have different opinions and conflict with each other. Therefore, companies invite outside specialized consultants as facilitators and organize workshops with stakeholders to try to compile fair and objective DX requirements. However, not all of the stakeholders who participated in the workshop are satisfied with the results compiled by the consultant. In this paper, we propose to incorporate an augmented digital process of text mining for the content of stakeholders' requirements into the conventional real in-person discussion process of organizing requirements. To evaluate the usefulness of this proposal, we conducted an industrial experiment analyzing stakeholders’ opinions collected during a workshop at an industrial company. In other words, we evaluated whether or not it is possible to discover new insights and potential requirements that the consultant was not aware of. As a result of this experiment, we were able to confirm the possibility of revealing stakeholder opinions that were overlooked by the consultant during the workshop at the time, doing so by applying the proposed approach. From this, we concluded that incorporating text mining into the organization of stakeholder requirements would be effective in the requirements gathering process of DX strategy development. [ABSTRACT FROM AUTHOR]

Over the past two decades, a number of advances in topic modeling have produced sophisticated models that are capable of generating topic hierarchies. In particular, hierarchical Latent Dirichlet Allocation (hLDA) builds a topic tree based on the nested Chinese Restaurant Process (nCRP) or other sampling processes to generate a topic hierarchy that allows arbitrarily large branch structures and adaptive dataset growth. In addition, hierarchical topic models based on the latent tree model, such as Hierarchical Latent Tree Analysis (HLTA), have been developed over the last five years. However, these models do not work well in cases with millions of documents and hundreds of thousands of terms. In addition, the topic trees generated by these models are always poorly interpretable, and the relationships among topics in different levels are relatively simple. The biomedical literature, including Medline abstracts, has large-scale documents in two major categories: biological laboratory research and medical clinical research. We propose a top-down binary hierarchical topic model (biHTM) for biomedical literature by iteratively applying a flat topic model and adaptively processing subtrees of the hierarchy. The biHTM topic hierarchy of complete Medline abstracts with more than 14 topic node levels shows good bimodality and interpretability. Compared to hLDA and HLTA, biHTM shows promising results in experiments assessed in terms of runtime and quality.

In recent years, short texts have become a kind of prevalent text on the internet. Due to the short length of each text, conventional topic models for short texts suffer from the sparsity of word co-occurrence information. Researchers have proposed different kinds of customized topic models for short texts by providing additional word co-occurrence information. However, these models cannot incorporate sufficient semantic word co-occurrence information and may bring additional noisy information. To address these issues, we propose a self-aggregated topic model incorporating document embeddings. Aggregating short texts into long documents according to document embeddings can provide sufficient word co-occurrence information and avoid incorporating non-semantic word co-occurrence information. However, document embeddings of short texts contain a lot of noisy information resulting from the sparsity of word co-occurrence information. So we discard noisy information by changing the document embeddings into global and local semantic information. The global semantic information is the similarity probability distribution on the entire dataset and the local semantic information is the distances of similar short texts. Then we adopt a nested Chinese restaurant process to incorporate these two kinds of information. Finally, we compare our model to several state-of-the-art models on four real-world short texts corpus. The experiment results show that our model achieves better performances in terms of topic coherence and classification accuracy. [ABSTRACT FROM AUTHOR]

Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications. Due to the length limitation, conventional topic models based on document-level word co-occurrence information often fail to distill semantically coherent topics from short text collections. On the other hand, word embeddings as a powerful tool have been successfully applied in natural language processing. Word embeddings trained on large corpus are encoded with general semantic and syntactic information of words, and hence they can be leveraged to guide topic modeling for short text collections as supplementary information for sparse co-occurrence patterns. However, word embeddings are trained on large external corpus and the encoded information is not necessarily suitable for training data set of topic models, which is ignored by most existing models. In this article, we propose a novel global and local word embedding-based topic model (GLTM) for short texts. In the GLTM, we train global word embeddings from large external corpus and employ the continuous skip-gram model with negative sampling (SGNS) to obtain local word embeddings. Utilizing both the global and local word embeddings, the GLTM can distill semantic relatedness information between words which can be further leveraged by Gibbs sampler in the inference process to strengthen semantic coherence of topics. Compared with five state-of-the-art short text topic models on four real-world short text collections, the proposed GLTM exhibits the superiority in most cases.

In the past decades, there have been a number of proposals to apply topic modeling to research trend analysis. However, most of previous studies have relied primarily on document publication year and have not incorporated the impact of articles into trend analysis. Unlike previous trend analysis using topic modeling, we incorporate citation count, which can be viewed as the impact of articles, into trend analysis to shed a new light on the understanding of research trends. To this end, we propose the Generalized Dirichlet multinomial regression (g-DMR) topic model, which improves the DMR topic model by replacing a linear inner product in topic priors, exp x d · λ t , with a more general form based on topic distribution function (TDF), exp f x d + ε . We use multidimensional Legendre Polynomial as TDF to capture publication year and the number of citations per publication simultaneously. In DMR model, since metadata could affect the document-topic distribution only monotonically and continuous values such as publication year and citation count need to be discretized, it is difficult to view the dynamic change of each topic. But the g-DMR model can handle various orthogonal continuous variables with arbitrary order of polynomial, so it can show more dynamic topic trends. Two major experiments show that the proposed model is better suited for topic generation with consideration of citation impact than DMR does for the trend analysis in the field of Library and Information Science in general and Text Mining in particular. [ABSTRACT FROM AUTHOR]

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental and human losses. The unpredictable nature of natural disasters behaviour makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyse public concerns during natural disasters; however, this approach is limited, expensive and time-consuming. Luckily, the advent of social media has provided scholars with an alternative means of analysing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasises the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modelling to create a better SA for disaster preparedness, response and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evidence a method of understanding the intentions of taste expressions from word-of-mouth data of cooking recipe websites using text mining. This study aims to clarify the use of the word "KOKU" as an example to verify the method. KOKU is one of the taste expressions used like richness experienced in various dishes such as in the taste of wine. In order to clarify the relationship between the features of KOKU and cooking categories, they were clustered using the latent Dirichlet allocation. The categories were classified into groups of foods using similar ingredients, sweetness, oils, and seasonings. Through the analysis mentioned above, the features of KOKU were defined. In the past, there has been no attempt to clarify the features of KOKU using word-of-mouth data from cooking recipe websites. The success in defining "KOKU" is evidence that this method has potential to be extended and applied to expressions other than KOKU. [ABSTRACT FROM AUTHOR]

The uncertainty in the financial market, whether the US--China trade war will slow down the global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets. In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model that uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events. The correlation was higher between the volatility of the market and uncertainty indices with larger expected supervised signal compared to uncertainty indices with the smaller expected supervised signal. We also applied the impulse response function to analyze the impact of the uncertainty indices on financial markets. [ABSTRACT FROM AUTHOR]

With the proliferation of the Internet and the rapid growth of electronic articles, text classification has become one of the key and important tools for data organization and management. In text classification a set of basic knowledge is provided to the system by learning. Then, new input documents enter to one of the subject groups. In health literature due to wide variety of topics, preparing such a set of early education is a very time consuming and costly task. The purpose of this article is to present a hybrid model of learning (supervised and unsupervised) for the subject classification of health scientific products that performs the classification operation without the need for an initial labeled set. To extract the thematic model of health science texts from 2009 to 2019 at PubMed database, data mining and text mining were performed using machine learning. Based on Latent Dirichlet Allocation model, the data were analyzed and then the Support Vector Machine was used to classify the texts. In the findings of this study, the model was introduced in three main steps. In data preprocessing, the unnecessary words were eliminated from the data set and the accuracy of the proposed model increased. In the second step, the themes in the texts were extracted using the Latent Dirichlet Allocation method, and as a basic training set in step 3, the data were backed up by the Support Vector Machine algorithm and the classifier learning was performed with the help of these topics. Finally, with the help of the classification, the subject of each document was identified. The results showed that the proposed model can build a better classification by combining unsupervised clustering properties and prior knowledge of the samples. Clustering on labeled samples with a specific similarity criterion merges related texts with prior knowledge, and the learning algorithm teaches classification by supervisory method. Combining classification and clustering can increase the accuracy of classification of health texts. [ABSTRACT FROM AUTHOR]

In recent years, short texts have become a kind of prevalent text on the internet. Due to the short length of each text, conventional topic models for short texts suffer from the sparsity of word co-occurrence information. Researchers have proposed different kinds of customized topic models for short texts by providing additional word co-occurrence information. However, these models cannot incorporate sufficient semantic word co-occurrence information and may bring additional noisy information. To address these issues, we propose a self-aggregated topic model incorporating document embeddings. Aggregating short texts into long documents according to document embeddings can provide sufficient word co-occurrence information and avoid incorporating non-semantic word co-occurrence information. However, document embeddings of short texts contain a lot of noisy information resulting from the sparsity of word co-occurrence information. So we discard noisy information by changing the document embeddings into global and local semantic information. The global semantic information is the similarity probability distribution on the entire dataset and the local semantic information is the distances of similar short texts. Then we adopt a nested Chinese restaurant process to incorporate these two kinds of information. Finally, we compare our model to several state-of-the-art models on four real-world short texts corpus. The experiment results show that our model achieves better performances in terms of topic coherence and classification accuracy.