Topic Extraction Research Articles

데이터로 본 판소리의 性 인지 체계: 신재효本을 중심으로

This study analyzes the sexual cognitive system in pansori works using Shin Jae-hyo’s pansori texts as the research subject. To that end, I first extracted excerpts of sexual expressions from Shin Jae-hyo’s pansori texts such as Chunhyang-ga(NamChang), Simcheong-ga, Bak-Taryeong, Tobyol-ga, Jeokbyeok-ga, and Byeongangsoe-ga. Then, I input them on a sentence-by-sentence basis with metadata including the speaker (narrator), target of description, related characters, works, and excerpts. As a preliminary step for the quantitative analysis, I performed morphological analysis and preprocessing on the data entered in the text column. This made the language of the pansori texts suitable for digital analysis tools with the input of new information or stop words into the user dictionary.
 Three analyses were attempted using the data extracted from Shin Jae-hyo’s pansori texts. First, basic statistical analysis was conducted to visualize which work had the most occurrences of sexual expressions and to examine the frequency of speaker-description target pairs when sexual expressions were uttered and their targets. Second, Latent Dirichlet Allocation (LDA) topic modeling was performed to extract topics from a large collection of documents. Third, I used co-occurrence word analysis to reveal the semantic network of words constituting sexual expressions.
 The findings revealed that the sexual cognitive system in pansori works is based on “deviation and return.” That is, while enjoying pansori, one can realize that forbidden desires are, indeed, common desires and can satisfy sexual fantasies. However, such desires should not persist beyond exceptional circumstances and should ultimately be concealed and transformed to abide byto societal norms. Listening to and reading pansori involves repeatedly deviating from and returning to sexuality as a theme that persists throughout the whole story.

Scoring method of English composition integrating deep learning in higher vocational colleges

Along with the progress of natural language processing technology and deep learning, the subjectivity, slow feedback, and long grading time of traditional English essay grading have been addressed. Intelligent English automatic scoring has been widely concerned by scholars. Given the limitations of topic relevance feature extraction methods and traditional automatic grading methods for English compositions, a topic decision model is proposed to calculate the topic relevance score of the topic richness in English composition. Then, based on the Score of Relevance Based on Topic Richness (TRSR) calculation method, an intelligent English composition scoring method combining artificial feature extraction and deep learning is designed. From the findings, the Topic Decision (TD) model achieved the best effect only when it was iterated 80 times. The corresponding accuracy, recall and F1 value were 0.97, 0.93 and 0.95 respectively. The model training loss finally stabilized at 0.03. The Intelligent English Composition Grading Method Integrating Deep Learning (DLIECG) method has the best overall performance and the best performance on dataset P. To sum up, the intelligent English composition scoring method has better effectiveness and reliability.

Post-discharge nutritional management for patients with coronary heart disease and frailty: a qualitative study

BackgroundFrail elderly patients experience physiological function and reserve depletion, leading to imbalances in their internal environment, which increases the risk of coronary heart disease recurrence and malnutrition. However, the majority of these patients, who primarily have a low level of education and lack self-management skills, face difficulties actively dealing with obstacles during the transition period after their discharge from hospitalization. Therefore, it is necessary to understand and discuss in depth the nutrition management experience of discharged elderly patients with coronary heart disease and frailty (ages 65-80 years old) and to analyze the promoting and hindering factors that affect scientific diet behavior during the discharge transition period.MethodsFifteen elderly patients with coronary heart disease and frailty who had been discharged from the hospital for 6 months were interviewed using a semistructured method. The directed content analysis approach to descriptive research was used to extract topics from the interview content.ResultsAll participants discussed the problems in health nutrition management experience of discharged. Five topics and ten subtopics were extracted, such as ①Weak perceptions and behaviors towards healthy eating (personal habit solidification, negative attitudes towards nutrition management), ②Lack of objective factors for independently adjusting dietary conditions (reliance on subjective feelings, times of appetite change), ③Personal hindrance factors (memory impairment, deficiencies in self-nutrition management), ④Expected external support (assistance care support, ways to obtain nutritional information), ⑤Lack of continuous nutrition management (interruption of professional guidance, avoidance of medical treatment behavior).ConclusionsNutrition management after discharge places a burden on elderly patients with coronary heart disease and frailty. According to the patients' physical conditions, we should develop a diet support system that is coordinated by individuals, families and society.

Automatic label curation from large-scale text corpus

Abstract The topic modeling technique extracts themes based on their probabilistic measurements from any large-scale text collection. Even though topic modeling pulls out the most important phrases that describe latent themes in text collections, a suitable label has yet to be found. Interpreting the topics extracted from a text corpus and identifying a suitable label automatically reduces the cognitive load for the analyst. Extractive methods are used typically to select a label from a given candidate set, based on probability metrics for each candidate set. Some of the existing approaches use phrases, words, and images to generate labels using frequency counts of different words in the text. The paper proposes a method to generate labels automatically to represent each topic based on a labeling strategy to filter candidate labels and then apply sequence-to-sequence labelers. The objective of the method is to get a meaningful label for the result of the Latent Dirichlet Allocation algorithm. The BERTScore metric is used to evaluate the effectiveness of the proposed method. The proposed method generates good interpretative labels as compared to baseline models for topic words or terms automatically. The comparison with the label generated through ChatGPT API shows the quality of the generated label with the experiment performed on Four Datasets NIPS, Kindle, PUBMED, and CORD-19.

A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task.

A Multi-Metric Model for analyzing and comparing extractive text summarization approaches and algorithms on scientific papers

In today's world, where data and information are increasingly proliferating, text summarization and technologies play a critical role in making large amounts of text data more accessible and meaningful. In business, the news industry, academic research, and many other fields, text summarization helps make quick decisions, access information faster, and manage resources more effectively. Additionally, text summarization research is conducted to further improve these technologies and develop new methods and algorithms to provide better summarization of texts. Therefore, text summarization and research in this field are of great importance in the information age. In this study, a new operating model for text summarization that can be applied to different algorithms is proposed and evaluated. Sixteen summarization algorithms covering six approaches (statistical, graph-based, content-based, pointer-based, position-based, and user-oriented) were implemented and tested on 50 different full-text article datasets. Four evaluation criteria (BLEU, Rouge-N, Rouge-L, METEOR) were used to assess the similarity between the generated summaries and the original summaries. The performance of the algorithms within each approach was averaged and the overall best-performing algorithm was selected. This best algorithm was subjected to further analysis through Topic Modelling and Keyword Extraction to identify key topics and keywords within the summarised text. The proposed model provides a standardized workflow for developing and thoroughly testing summarization algorithms across datasets and evaluation metrics to determine the most appropriate summarization approach. This study demonstrates the effectiveness of the model on a variety of algorithm types and text sources.

Topic Extraction: BERTopic’s Insight into the 117th Congress’s Twitterverse

As social media (SM) becomes increasingly prevalent, its impact on society is expected to grow accordingly. While SM has brought positive transformations, it has also amplified pre-existing issues such as misinformation, echo chambers, manipulation, and propaganda. A thorough comprehension of this impact, aided by state-of-the-art analytical tools and by an awareness of societal biases and complexities, enables us to anticipate and mitigate the potential negative effects. One such tool is BERTopic, a novel deep-learning algorithm developed for Topic Mining, which has been shown to offer significant advantages over traditional methods like Latent Dirichlet Allocation (LDA), particularly in terms of its high modularity, which allows for extensive personalization at each stage of the topic modeling process. In this study, we hypothesize that BERTopic, when optimized for Twitter data, can provide a more coherent and stable topic modeling. We began by conducting a review of the literature on topic-mining approaches for short-text data. Using this knowledge, we explored the potential for optimizing BERTopic and analyzed its effectiveness. Our focus was on Twitter data spanning the two years of the 117th US Congress. We evaluated BERTopic’s performance using coherence, perplexity, diversity, and stability scores, finding significant improvements over traditional methods and the default parameters for this tool. We discovered that improvements are possible in BERTopic’s coherence and stability. We also identified the major topics of this Congress, which include abortion, student debt, and Judge Ketanji Brown Jackson. Additionally, we describe a simple application we developed for a better visualization of Congress topics.

Dendrobium officinale flowers' topical extracts improve skin oxidative stress and aging.

Dendrobium officinale flowers (DOF) have the effects of antiaging and nourishing yin, but it lacks pharmacological research on skin aging. Confirming the role of DOF in delaying skin aging based on the "in vitro animal-human" model. In this experiment, three kinds of free radical scavenging experiments invitro, D-galactose-induced aging mouse model, and human antiaging efficacy test were used to test whether DOF can improve skin aging through anti-oxidation. In vitro experiment shows that DOF has certain scavenging effect on 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical, hydroxyl free radical, and superoxide free radical, and its IC50 is 0.2090 μg/mL, 15.020, and 1.217 mg/mL respectively. DOF can enhance the activities of T-AOC, SOD, CAT, and GSH Px in the serum of aging mice, increase the content of GSH, and reduce the content of MDA when administered with DOF of 1.0, 2.0, and 4.0 g/kg for 6 weeks. In addition, it can enhance the activity of SOD in the skin of aging mice, increase the content of Hyp, and decrease the content of MDA, activated Keap1/Nrf2 pathway in the skin of aging mice. Applying DOF with a concentration of 0.2 g/mL on the face for 8 weeks can significantly improve the skin water score and elasticity value, reduce facial wrinkles, pores, acne, and UV spots, and improve the facial brown spots and roughness. DOF can significantly improve skin aging caused by oxidative stress, and its mechanism may be related to scavenging free radicals in the body and improving skin quality.

Navigating Success in Carbon Offset Projects: A Deep Dive into the Determinants Using Topic Modeling

Carbon offset projects play a crucial role in tackling the global challenge of climate change. However, there is limited understanding of the factors contributing to the success of a carbon offset project. In this study, we utilize the latent Dirichlet allocation method to extract topics from the descriptions of carbon offset projects sourced from the Gold Standard Foundation. Our findings reveal that projects encompassing both safety and efficient energy solutions for households command higher prices. These results imply that an effective carbon offset project should mitigate individual household emissions while enhancing safety. Our research carries significant implications for stakeholders involved in carbon offset projects and can serve as a foundation for policy formulation and standard regulations.

THE EFFECT OF ALOE VERA TOPICAL GEL EXTRACTION ON TGF-β1 EXPRESSION IN THERMAL BURN PALPEBRA GRADE II RATS MODEL

Abstract
 Introduction & Objectives : Grade II palpebral burns are burns that reach the epidermis and part of the dermis of palpebrae and can have impaired wound healing. TGF β1 has the broadest spectrum of action, affecting all cell types involved in all stages of wound healing. Decreased expression of TGF β1 is associated with impaired wound healing. Aloe Vera has long been known in Indonesia as a healing plant. Aloe Vera extract contains a variety of bioactive compounds that play a role in stimulating wound healing. The administration of Aloe Vera extract on thermal burns aims to reduce inflammation, increase collagen content, and increase the rate of re-epithelization. The administration of Aloe Vera gel extract allows faster healing of thermal burns and rebuilds the vascularity of the wound tissue.
 Methods : Objective: To determine the expression of Transforming Growth Factor Beta 1 (TGF β1) in a rat model of thermal burn injury grade II of the eyelids treated with topical Aloe Vera gel extract with the concentrations of 20%, 40%, Balanced Salt Sodium (BSS) and a control group that was not given burn intervention.
 Results : Methodology: an experimental study with the post-test-only approach with a control group design. 24 Rats have induced grade II eyelid thermal burns, divided into four groups: Normal (without treatment), negative control (BSS), given Aloe vera 20%, 40%. Then TGF β1expression was quantified
 Conclusion : The highest rise in TGF β1expression was found in the group with 40% Aloe vera gel, followed by 20% Aloe vera gel compared to the negative control (BSS Balanced Salt)

Utilizing Topic Modeling to Establish Sustainable Public Policies by Analyzing Korea’s Sports Human Rights over the Last Two Decades

Human rights violations in sports reveal that athletes are continuously exposed to violence. In Korea, the negative effects of its sports powerhouse paradigm are increasingly apparent. Although discussions on human rights in sports have progressed, academic research on this has not. Sourcing information from Korea’s Press Foundation’s big data news system from 2006 to 2023, this study used term frequency, term frequency-inverse document modeling, topic term matrix extraction through latent Dirichlet allocation (LDA), and LDAvis to analyze sports human rights issues and policies over time. The results revealed topics in three timeframes: in the first policy establishment period (2006–2010), the topics ranged from “Sports Human Rights Education” to “Minimum Education for Student Athletes”. In the policy transition period (2011–2018), the topics included “Strengthening Sports Human Rights Education”, “Women’s Human Rights Issues in Sports”, and “Government-Level Investigation into Sportsdom Controversies”; and in the second policy establishment period (2019–2023), the topics included “Athlete Harassment”, “Sportsdom #MeToo Movement”, “Guarantee of Student Human Rights Convenience Facilities”, and “Guarantee of Sports Human Rights”. Better mid- to long-term plans, national efforts, and education that improve awareness of human rights in sports are needed for sustainability.

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.

Exploring the Role of Pleurotus Ostreatus as an Ointment Formulation in Inducing Wound Healing in Mice Skin.

Natural compounds rich in secondary metabolites have gained attention as alternative therapies for wound healing due to their potential advantages over conventional treatments. Pleurotus ostreatus mushrooms have been identified for their wound-healing properties, including promoting neovascularization, epithelialization, and collagen synthesis. This study aimed to investigate the wound-healing properties of different doses of topical Extract of Pleurotus ostreatus in albino mice using an excisional wound model. The experimental design involved administering various concentrations of the extract to evaluate its effects on wound closure and histopathological and immunohistochemical parameters. The results revealed significant wound closure and improvements in histopathological and immunohistochemical parameters in mice treated with Pleurotus ostreatus extract. These findings suggest the potential of Pleurotus ostreatus extract as a viable wound healing agent. Pleurotus ostreatus extract demonstrates promising wound-healing capabilities, including promoting wound closure and enhancing histopathological and immunohistochemical parameters in albino mice. Further research is warranted to explore its full therapeutic potential and mechanism of action in wound healing.

구조적 토픽 모형을 활용한 기초학력 관련 담론 및 정책 동향 분석

Purpose: This study aims to investigate discourse and policy trends in basic academic achievement
 by analyzing texts associated with the subject. Exploring main topics and their changes in response
 to the dynamics of basic academic achievement guarantee policies will provide insights that can
 contribute to the development and improvement of relating policies.
 Methods: Structural Topic Modeling (STM) was employed to extract topics in analyzing academic
 journal articles, Ministry of Education press releases, and media articles. The topic prevalence was
 estimated and statistically tested in relation to the phases of the basic academic achievement guarantee
 policy, namely growth period, transition period, and confusion period. These policy phases served
 as metadata for the analysis.
 Results: Seven topics were extracted from academic journal articles, including academic achievement
 assessment, the characteristics of underachievers, and basic academic achievement policies. Another
 seven topics emerged in the Ministry of Education press releases, including basic academic achievement
 improvement programs, customized support for educational welfare, and online class and learning loss
 support. In media articles, nine topics were identified, including basic academic achievement
 improvement programs, support for linking specialized institutions, and controversies over how to
 conduct diagnostic evaluation. The prevalence of these topics changed over the policy phases.
 Conclusion: The study suggests that research on learner characteristics and vulnerable groups should
 be continued and expanded. It also emphasizes the importance of establishing long-term policies,
 fostering social consensus to strengthen the diagnosis of basic academic achievement, and providing
 support to enhance teachers’ expertise on basic academic achievement.

Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion

Abstract Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA) or neural topic models, follow the same underlying approach of topic interpretability and topic extraction. We propose a method that incorporates a deeper understanding of both sentence and document themes, and goes beyond simply analyzing word frequencies in the data. Through simple corpus expansion, our model can detect latent topics that may include uncommon words or neologisms, as well as words not present in the documents themselves. Additionally, we propose several new evaluation metrics based on intruder words and similarity measures in the semantic space. We present correlation coefficients with human identification of intruder words and achieve near-human level results at the word-intrusion task. We demonstrate the competitive performance of our method with a large benchmark study, and achieve superior results compared to state-of-the-art topic modeling and document clustering models.

A Comparative Analysis of Text Mining Methodologies for Online Consumer Reviews

Extracting meaningful insights from the sheer volume of Online Consumer Reviews (OCRs) has been challenging. We aim to explore the most effective methodologies for text mining of OCRs, covering topic extraction, topic classification, and sentiment analysis. Through a comprehensive review of recent research on text mining applied to OCRs, we found that LDA2Vec can enhance the effectiveness of conventional LDA for topic extraction. Additionally, the combination of Convolutional Neural Networks (CNN) and GloVe demonstrates the best performance for topic classification, while CNN and SVM outperform other algorithms for sentiment analysis. Furthermore, the spaCy Natural Language Processing (NLP) proves to be a more effective choice for text pre-processing compared to Natural Language Toolkit (NLTK). Subsequently, we applied these refined models to a Yelp reviews dataset, assessed their performance against conventional models, and provided a comprehensive discussion of the results and limitations. The insights gained from this study can be valuable for developing effective models in OCR analysis.

Identifying hidden patterns of fake COVID-19 news: An in-depth sentiment analysis and topic modeling approach

Spreading misinformation and fake news about COVID-19 has become a critical concern. It contributes to a lack of trust in public health authorities, hinders actions from controlling the virus’s spread, and risks people’s lives. This study aims to gain insights into the types of misinformation spread and develop an in-depth analytical approach for analyzing COVID-19 fake news. It combines the idea of Sentiment Analysis (SA) and Topic Modeling (TM) to improve the accuracy of topic extraction from a large volume of unstructured texts by considering the sentiment of the words. A dataset containing 10,254 news headlines from various sources was collected and prepared, and rule-based SA was applied to label the dataset with three sentiment tags. Among the TM models evaluated, Latent Dirichlet Allocation (LDA) demonstrated the highest coherence score of 0.66 for 20 coherent negative sentiment-based topics and 0.573 for 18 coherent positive fake news topics, outperforming Non-negative Matrix Factorization (NMF) (coherence: 0.43) and Latent Semantic Analysis (LSA) (coherence: 0.40). The topics extracted from the experiments highlight that misinformation primarily revolves around the COVID vaccine, crime, quarantine, medicine, and political and social aspects. This research offers insight into the effects of COVID-19 fake news, provides a valuable method for detecting and analyzing misinformation, and emphasizes the importance of understanding the patterns and themes of fake news for protecting public health and promoting scientific accuracy. Moreover, it can aid in developing real-time monitoring systems to combat misinformation, extending beyond COVID-19-related fake news and enhancing the applicability of the findings.

The communication reaction function of the European Central Bank. An analysis using topic modelling

ABSTRACT Central bank communication plays a crucial role in the conduct of monetary policy, yet the research on central bank communication, while growing, is still scarce. In this paper, we analyze the communication reaction function of the European Central Bank (ECB) through topic-based indices derived from the bank’s speeches. These indices are used as dependent variables in policy and communication reaction function models, as suggested by recent literature. The topics are extracted using Latent Dirichlet Allocation (LDA), a popular text mining algorithm for topic extraction. The ECB has recently reviewed its monetary policy strategy, which led to an increase in studies incorporating the new methods offered by text mining for analyzing the policy reaction function of the bank. We show how indices built through topic modelling can be used to study the communication reaction function of a central bank, and we examine which variables are significant for every topic communicated by the ECB.

MIT: Mutual Information Topic Model for Diverse Topic Extraction.

To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.

GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding

GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding