Topic Extraction Model Research Articles

International Development Trends in the Field of Agricultural Resources and the Environment

The development trends and research layout of agricultural resources and the environment (ARE) are the focus of global attention. In this study, we compiled a data set of SCI papers published in the ARE field since the 13th Five-Year Plan. Thereafter, the topic extraction model of Latent Dirichlet Allocation (LDA) was used to mine the text content so as to explore the research layout of global ARE. The results show that, between 2016 and the time of this study, 31,559 articles were published in the ARE field, exhibiting an overall upward trend. During this time, China and the United States were the main forces in paper publishing. The Chinese Academy of Sciences (CAS), Northwest Agriculture and Forestry University, and the US Department of Agriculture were the top three publishing institutions. Research institutes in China, the United States, Russia, Sweden, the Netherlands, Brazil, and Australia cooperated closely, and the CAS was at the center of the cooperation network. The clustering results of text topics based on the LDA model show that three topics—namely, the interaction mechanisms of plants, the rhizosphere, and microorganisms; the mechanisms and predictive evaluation of soil landslides or erosion; and the decomposition and interaction response of organic matter in agroforestry ecosystems—have been the hot research areas in this field since 2016. The management and efficient utilization of farmland nutrients, and the technology and mechanisms of agricultural waste resource utilization have become key research directions since 2019. The research layouts of China and the United States in this field were analyzed, and the similarities and differences were compared. In addition, suggestions for the future development of ARE are proposed. This study is of great significance for the overall development trend of ARE, the timely tracking of scientific research hotspots, outlining key research and development directions, and promoting scientific and technological innovation.

Research Frontiers in the Field of Agricultural Resources and the Environment

From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the “interaction mechanism of plant–rhizosphere–microbial diversity” was a hot research frontier in the years 2016–2021. The themes of “wastewater treatment technology and efficient utilization of water resources”, the “value-added utilization of agricultural wastes and sustainable development”, the “soil ecological response mechanism under agronomic management measures”, and the “mechanism of soil landslide, erosion, degradation and prediction evaluation” were judged as potential research frontiers. The theme of “ecosystems management and pollution control of agricultural and animal husbandry” was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.

WITHDRAWN: Extracting key topics from massive COVID-19 information on social networks: An integrated deep learning and LDA framework

Quickly discovering and processing unmeaningful information is especially important in the current social network, where public opinion is rapidly fermenting. In this study, a novel topic extraction framework is proposed to extract key topics for the special event of COVID-19 on social networks. First, huge and noisy COVID-19 information is pre-processed for our framework. Second, a model of key information identification is designed for COVID-19 information, which combines the deep learning model of Text Convolutional Neural Networks (TextCNN) with Bi-directional Long Short-Term Memory (BiLSTM) to perform a multi-class classification task. Finally, a model of key topic extraction is developed with the Latent Dirichlet Allocation (LDA) model, which is able to summarize topics and keywords from a large amount of information. we crawled the public opinion information of the COVID-19 on weibo as our dataset. experimental results demonstrate that our model outperforms the other benchmark models in identifying important information related to COVID-19. the key topic extraction model based on LDA can further divide key information into multiple topics ►And describe the topics through keywords for fast tracking of COVID-19 pandemic on social networks.

BMCR: A collaborator recommendation model based on Scholars’ academic level

Collaborator recommendation helps scholars find suitable collaborators, which is useful for the development of scientific research and the transformation of scientific and technological achievements. However, the gap between scholars’ academic level seriously affects the establishment of cooperative relations. In this paper, a new model named Best Matching Collaborator Recommendation (BMCR) is proposed. BMCR model looks for the best matching collaborators for scholars from a new perspective of academic level. First, we define the academic level index of scholars and cluster scholars with this index. Next, the research topics of scholars are extracted from their published papers by using the topic extraction model. Then, for each cluster, a scholar-topic graph is built with the scholars and their topics. Finally, given the target scholar, the biased random walk with restart algorithm is carried out on the scholar-topic graph to find potential collaborators. Experiments on the MAG, DBLP, C-DBLP datasets show that our model improves the feasibility of cooperation between the target scholars and recommended scholars. Compared with other methods, our model has improved precision rate, recall rate and F1 score by 5.3%, 2.5%, and 4% respectively. The experimental results indicate that scholars’ academic level is a key reference factor in the collaborator recommendation, and that scholars with similar academic level are more likely to achieve practical cooperation. The findings are not only of great significance in recommending collaborators to scholars, but also can be extended to other fields to solve the problem of collaborator recommendation.

Sentiment Analysis of online reviews based on LDA and AP-Bert model

The purpose of this paper is to construct a more accurate behavior matrix by using fine-grained aspect level emotion analysis method. Firstly, LDA topic extraction model is used to extract the topic of online review text, and the concerned attributes are extracted. According to the characteristics of online comments, a BERT emotion analysis model with enhanced pooling was proposed. Activation function layer and max-average pooling layer were designed to solve the over-fitting problem of BERT model in the process of emotion analysis. Finally, by combining LDA extraction results and AP-Bert sentiment analysis results, the proportion matrix is obtained. Experimental results show that the accuracy, recall rate and AUC value of AP-Bert model are better than those of the same type of model and original BERT model.

Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies

With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment

Live-stream shopping is developing rapidly, but the sales levels of live streaming products vary by different hosts. How to increase the sales volume of live streaming products has become a problem. Consumers’ purchase behavior in live streaming is determined by some subjective factors, and the persuasiveness of linguistic style affects this subjective judgment to a certain extent. Therefore, the persuasiveness of the hosts’ linguistic style will lead to changes in consumers’ purchase intentions, which will affect the sales volume of products sold in the live streaming. Based on Hovland’s persuasion model, Aristotle’s rhetoric skills, text analysis, Latent Dirichlet Allocation (LDA) topic extraction model and grounded theory, this study divides the host’s linguistic persuasive style in the social e-commerce environment into five types: appealing to personality, appealing to logic, appealing to emotion, appealing to reward, and appealing to exaggeration. Combined with the sales volume of the product, we establish a regression model, and obtain the influence results of the host’s various linguistic persuasive styles on the sales of live streaming products. The results show that: the linguistic persuasive style of appealing to personality has the greatest positive impact on the sales volume of live broadcast products, but the linguistic style of appealing to logic has a negative impact. Interestingly, the same linguistic style has different effects for different types of products: the linguistic style of appealing to exaggeration has a negative effect on the sales volume of apparel products, but it has a positive influence on the sales volume of digital electrical products. Therefore, different linguistic styles should be used for different product types.

Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks

Textual data and analysis can derive new insights and bring valuable business insights. These insights can be further leveraged by making better future business decisions. Sources that are used for text analysis in financial industry vary from internal word documents, email to external sources like social media, websites or open data. The system described in this paper will utilize data from social media (Twitter) and tweets related to Italian banks, in Italian. This system is based on open source tools (R language) and topic extraction model was created to gather valuable information. This paper describes methods used for data ingestion, modelling, visualizations of results and insights.

Analysis of risk factor domains in psychosis patient health records

BackgroundReadmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component.ResultsWe designed and evaluated multiple multilayer perceptron and radial basis function neural networks to predict the sentences in a patient’s EHR that are associated with one or more of seven readmission risk factor domains that we identified. In contrast to our baseline cosine similarity model that is based on the methodologies of prior works, our deep learning approaches achieved considerably better F1 scores (0.83 vs 0.66) while also being more scalable and computationally efficient with large volumes of data. Additionally, we found that integrating clinically relevant multiword expressions during preprocessing improves the accuracy of our models and allows for identifying a wider scope of training data in a semi-supervised setting.ConclusionWe created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show results for our topic extraction model and identify additional features we will be incorporating in the future.

Extracting and tracking hot topics of micro-blogs based on improved Latent Dirichlet Allocation

Micro-blog has changed people’s life, study, and work styles. Every day, we want to know what public opinion news happens and how it evolves. Extracting and tracking these topics correctly help us better understand the latest public opinions and pay attention to their evolution. To extract topics from Microblog posts accurately, we adopt five unique features of micro-blogs to drive the joint probability distributions of all words and topics, and improve LDA into our topic extraction model(named MF-LDA). To track evolution trend of the topic, we propose a hot topic life cycle model (named HTLCM). We divide the HTLCM into five stages, namely, birth, growth, maturity, decline, and disappearance. The HTLCM determines whether a topic is the candidate hot topic or not and estimates hot topic evolution stages. On the other hand, we propose a hot topic tracking (shorten for HTT) algorithm which integrates MF-LDA and HTLCM. First, the HTT assigns candidate hot topics, which are labeled by HTLCM, to the corresponding time window according to the release time. Second, to obtain the hot topic in each time window, we input Micro-blog posts of each time window into MF-LDA in order. By analyzing changes in these hot topics, we track the changes in their contents. The experiment results show that MF-LDA has a lower perplexity and higher coverage rate than LDA under the same conditions. We conclude parameters of the Transition regions of our proposed HTLCM model. The MR, FR of our proposed HTLCM model are lower than 18%. The average P, R, F of the HTT algorithm are 85.64%, 84.97%, 85.66%, respectively. A practical application on topicFemale driver beats male driver in chengdu shows an excellent effect and practical significance of HTLCM model and HTT algorithm in extracting and tracking hot topics.

Two-Stage Topic Extraction Model for Bibliometric Data Analysis Based on Word Embeddings and Clustering

Topic extraction is an essential task in bibliometric data analysis, data mining and knowledge discovery, which seeks to identify significant topics from text collections. The conventional topic extraction schemes require human intervention and involve also comprehensive pre-processing tasks to represent text collections in an appropriate way. In this paper, we present a two-stage framework for topic extraction from scientific literature. The presented scheme employs a two-staged procedure, where word embedding schemes have been utilized in conjunction with cluster analysis. To extract significant topics from text collections, we propose an improved word embedding scheme, which incorporates word vectors obtained by word2vec, POS2vec, word-position2vec and LDA2vec schemes. In the clustering phase, an improved clustering ensemble framework, which incorporates conventional clustering methods (i.e., k-means, k-modes, k-means++, self-organizing maps and DIANA algorithm) by means of the iterative voting consensus, has been presented. In the empirical analysis, we analyze a corpus containing 160,424 abstracts of articles from various disciplines, including agricultural engineering, economics, engineering and computer science. In the experimental analysis, performance of the proposed scheme has been compared to conventional baseline clustering methods (such as, k-means, k-modes, and k-means++), LDA-based topic modelling and conventional word embedding schemes. The empirical analysis reveals that ensemble word embedding scheme yields better predictive performance compared to the baseline word vectors for topic extraction. Ensemble clustering framework outperforms the baseline clustering methods. The results obtained by the proposed framework show an improvement in Jaccard coefficient, Folkes & Mallows measure and F1 score.

Automatic extraction of persistent topics from social text streams

Microblogging services allow users to publish their thoughts, activities, and interests in the form of text streams and to share them with others in a social network. A user's text stream in a microblogging service is temporally composed of the posts the user has written or republished from other socially connected users. In this context, most research on the microblogging service has primarily focused on social graph or topic extraction from the text streams, and in particular, several studies attempted to discover user's topics of interests from a text stream since the topics play a crucial role in user search, friend recommendation, and contextual advertisement. Yet, they did not yet fully address unique properties of the stream. In this paper, we study a problem of detecting the topics of long-term steady interests to a user from a text stream, considering its dynamic and social characteristics, and propose a graph-based topic extraction model. Extensive experiments have been carried out to investigate the effects of the proposed approach by using a real-world dataset, and the proposed model is shown to produce better performance than the existing alternatives.