Collection Of Textual Data Research Articles

Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community

Leveraging a large collection of textual data (N = 21,539) from a Chinese online community, we employed structural topic modeling to investigate the thematic disparities between professionals and laypersons, regarding the effectiveness of Traditional Chinese Medicine (TCM) on COVID-19. Findings reveal that laypersons are the dominant communicators in terms of discussion volume, who often focus on relevant news events, societal or political aspects of TCM. In contrast, professionals keep concentrating on issues related to medical expertise, and do not shift attentions as frequent as laypersons. Despite the dominant influence of professionals on laypersons’ agenda, two-way agenda interactions identified confirm that lay public is empowered to negotiate with elite professionals under certain topics. Our results provide novel insights into the dynamic nature of attentions, behaviors, and relations among prominent communication actors, and encourage future research to examine the individual-level and societal-level impacts of these constructs in the emerging online media landscape.

Latent Beta-Liouville Probabilistic Modeling for Bursty Topic Discovery in Textual Data

Topic modeling has become a fundamental technique for uncovering latent thematic structures within large collections of textual data. However, conventional models often struggle to capture the burstiness of topics. This characteristic, where the occurrence of a word increases its likelihood of subsequent appearances in a document, is fundamental in natural language processing. To address this gap, we introduce a novel topic modeling framework, integrating Beta-Liouville and Dirichlet Compound Multinomial distributions. Our approach, named Beta-Liouville Dirichlet Compound Multinomial Latent Dirichlet Allocation (BLDCMLDA), is designed to specifically model word burstiness and support a wide range of adaptable topic proportion patterns. Through experiments on diverse benchmark text datasets, the BLDCMLDA model has demonstrated superior performance over conventional models. Our promising results in terms of perplexity and coherence scores demonstrate the effectiveness of BLDCMLDA in capturing the nuances of word usage dynamics in natural language.

A Contrastive Study of the Comparative and Superlative Inflectional Morphemes of English and Yoruba Languages

This paper analyses and contrasts the comparative and superlative inflectional morphemes of the English and Yoruba Languages with the aim of identifying the morphological and syntactic structural differences, which may pose some difficulties to the Yoruba learners of English as a second language. The study adopted the Contrastive Analysis (CA) theory as its theoretical framework as well as the qualitative descriptive design with a content analysis approach, while using a textual data collection method. The study revealed that while the English language uses the inflectional markers “-er” to mark the comparatives and “-est” to mark the superlatives of adjectives and adverbs; the Yoruba language on the other hand employs lexical items “-ju…lo” as comparative marker and “-julo” for the superlative marker through partial reduplication. The paper further discovered that while the inflectional morphemes of the English language are all suffixes, the Yoruba language lends itself to both, but majorly prefixation. However, the comparative and superlative markers of both English and Yoruba languages are suffixes. In addition, the English language in its grammatical rules contains some exceptions or irregularities, which may further pose some challenges to the second language learner. The study therefore recommends that second language learners, whose first language is Yoruba, should ensure proper learning and acquisition of the correct formation and usage of all the comparative and superlative markers of the English language, in order to improve their proficiency in the language.

АНАЛІЗ ТОНАЛЬНОСТІ ТЕКСТІВ НА ОСНОВІ МЕТОДІВ МАШИННОГО НАВЧАННЯ ДЛЯ МОНІТОРИНГУ СУСПІЛЬНИХ НАСТРОЇВ ЩОДО КОРУПЦІЇ

Monitoring public attitudes towards corruption is critical for governments, organisations and researchers seeking to understand the causes of its spread and ways to prevent and counteract these destructive processes in society. The article is devoted to analysing the corruption-related tone of media publications and making an analysis of the content on corruption in the world and in Ukraine. The relevance of solving this scientific problem is due to the fact that the use of machine learning methods allows processing large amounts of data from various sources and obtaining a detailed and dynamic understanding of public attitudes towards corruption. The article analyses the areas of application of sentiment analysis at different levels of economic relations. Machine learning methods, namely the VADER analyser, were used to study the tone of the text of publications about corruption. The articles published in The Guardian were selected for analysis. The study period was 2021– 2024. For the analysis of the text tone, Orange Data Mining was chosen. The study of the issue of tone of texts in the media in the article is carried out in the following logical sequence: collection of text data on corruption, data pre-processing (cleaning and preparing the text for classification), transformation of textual data into numerical representations for machine learning models, and sentiment classification based on machine learning methods. The study found that during the first six months of the year with about 910 publications on corruption issues were published annually. One third of the corruption stories in The Guardian in 2022 mentioned Ukraine. The analysis showed that the dominant emotional tone of articles about corruption in the world was negative. At the same time, the emotional colouring of news texts on corruption in Ukraine is more negative compared to world news on similar issues. The results of the study are of practical importance and can be used by non-governmental organizations, government institutions and international organisations in assessing the effectiveness of policies to prevent and combat corruption in the country.

Broken psychological contracts in health and social care during COVID-19 among nursing professionals.

In this study we are interested about the reasons why nurses leave their occupation as a nurse and what is the role of the COVID-19 pandemic behind this decision. Our focus is in registered and auxiliary nurses working in all fields of health and social care. The research, which this abstract is based, investigates why nursing professionals are leaving their occupation and how the health care as working environment should be developed in order to secure enough enthusiastic professionals to the field. Theoretical background leans on the concept of psychological contract which we using to describe the relationship between employer and employee referring to an individual´s beliefs about the terms and conditions of a mutual exchange agreement.
 Psychological contract exchange is based on the perceptions of employee and employer rather than what is explicitly agreed and written down. The studies of the psychological contract have emphasized to breach of the contract meaning the situation where another contract party has failed to fulfill the expectations and obligations of the other party. Psychological contract breach cause dissatisfaction towards to work, impairs commitment to the organization and affects to the employee´s mental well-being and retention to work. In this study we examine situation where the psychological contracts breach has already led to leave work in health and social care. Explicit research question is: Why nurses leave their nursing position and how the COVID-19 has influenced to this decision?
 Our data consists of 36 stories wrote by registered and auxiliary nurses telling why they have left their nursing position and it was collected in Autumn 2021 via textual data collection tool “Penna” which is maintaining by Finnish Social Science Data Archive (FSD). Written request was intentionally very open and gave respondents freedom to describe their thoughts why they have left the nursing position. Altogether there were 310 stories but in here, we decided to concentrate to stories (n=36) where the COVID-19 was mentioned to be the final reason to leave the field. Based on preliminary findings, it seems that psychological contract was breached many ways during the COVID-19. For example, nurses were strongly disappointed how the work was organized during the crisis (leadership issues, working environment). They felt that their self-determination was broken especially considering the vaccination policy. In these stories, the writers emphasize the workload and lack of appreciation.
 This study highlighted that there were critical perspectives towards health and social care working conditions that the COVID-19 made even worse. This means that the was negativity and feeling of unappreciation and the COVID-19 somehow enabled to final decision. This is appearing as increase in workload and nurses were not able to have their normal and entitled vacations. Based on preliminary findings, it seems that psychological contract breached.
 Study indicates that we need to recognize the factors that are breaching the psychological contracts in order to develop and plan health and social care as well as nursing profession. This should be considered in all levels: individual, leadership and management as we

Text Mining of Journal Article Titles: An LDA-Based Topic Modeling Approach

Among the techniques of text mining, topic modeling is considered one of the emerging tools to extract or detect hidden themes that lie within a huge collection of textual data. Latent Dirichlet Allocation (LDA) is considered a popular method in the field of topic modeling. This paper deals with topic modeling from 9130 articles of Sri Lankan authors having a minimum of 5 citations downloaded from the WoS database using LDA. The LDA tuning (R package) is used in the study to take various measurements for deciding subjects in light of factual elements. The top 10 latent topics were identified, and different unique terms associated with the topics were also discussed. Health is traced as the most occurring latent topic followed by forest and solar cells. Topic-1 (100%) Contains Water-related terms, which is around 60%; Irrigation and soilrelated were 40% (1997). This first topic was prominent across the period barring 1994 and 1996. Topic 3 has gradually decreased and Topic 9 has gradually increased during the last five decades. By comparing our results to traditional scholarship by Sri Lankan authors and the evolution of scientific publication by the island nation, we have shown that topic models can emerge as a scientific alternative to conventional classification systems.

A crowdsource based framework for Bengali scene text data collection and detection

Scene texts are the type of texts that we see in our surroundings when we are outside. They have custom fonts, sizes, shapes and colors that are often different from the fonts or styles typically found in documents. Scene text recognition has grown in popularity due to its significance in the development of smarter automated systems. Despite the fact that numerous types of research on scene text detection and recognition have been conducted in different languages, similar work on the Bengali language is limited by the lack of large-scale usable datasets. In this paper, we propose a multipurpose system for Bengali Scene text that allows for easy dataset collection via crowdsourcing as well as annotation, classification and detection tasks to be performed on the same platform. It comes with an Android-based application that can be used to capture or load images from the device, select Bengali texts over the images and annotate them. The images and labels are stored in the cloud. A Python-based script is used to perform real-time data processing, analysis, and detection tasks. The proposed system has also been tested on a crowdsourced Bengali scene text dataset collected by this system. The classification model achieved an accuracy score of 97%, and the detection model achieved a Mean Average Precision score of 92 on the on these crowd contributed dataset.

MapIntel: A visual analytics platform for competitive intelligence

AbstractCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision‐making. The system searching module uses a retriever and re‐ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross‐encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer‐based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

A hunger-based scheduling strategy for distributed crawler

Distributed crawling is one of the mainstream text data collection technologies, which is essential for mining boundless data available on the Internet for users. Internet information is clustered by the correlation of keywords, and users employ search engines to retrieve relevant keywords to get the information they care about. Distributed crawlers are used to mine the Internet information by simulating users’ behavior, the more important keywords that users care about, the higher the correlation of data to keywords. In order to preferentially collect information that users care about with minimal resource consumption, in this paper, we design a scheduling framework and propose a novel scheduling strategy based on hunger for distributed crawler. We first define the load capacity of distributed crawler as hunger which reflects the ability to complete tasks and divide keywords queues into sub-queues based on the hunger of distributed crawlers. Then, we use a vector space model and cosine similarity algorithm to learn the correlation of keywords to text data and apply the optimized logistic algorithm to measure the importance of keywords. Meanwhile, we design a comprehensive evaluation algorithm to quantify the contribution of keywords, so that updating sub-queues order. Finally, new sub-queues are used in the deeper scheduling to preferentially get data that users desire and sacrifice the least number of resources. Experimental results demonstrate that our method optimizes the scheduling procedures and makes crawling more efficient with less run time.

Chat Generative Pretrained Transformer to optimize accessibility for cataract surgery postoperative management

Abstract Cataract surgery is one of the most common surgeries (over 3 million cases) in the United States per year. Consequently, there are multiple diverse and unique postoperative questions in the postoperative course following cataract extraction. To provide rapid and accessible guidance, large language models may help optimize this area in cataract surgery. Chat Generative Pretrained Transformer (GPT) is a complex conversational artificial intelligence model built by OpenAI and trained using an extensive collection of textual data sourced from the internet. The utilization of ChatGPT has a significant potential for diverse applications within various academic disciplines. In the field of medicine, it has the potential to aid health-care professionals, researchers, and patients through a variety of ways. We describe the use of ChatGPT to respond to common postoperative questions associated with cataract extraction. Although further research is required before more widespread acceptance and clinical implementation, we believe that GPT-4 has the potential to improve cataract and refractive postoperative care by providing immediate and accurate responses to patient queries.

Implementation of Machine Learning for Text Classification Using the Naive Bayes Algorithm in Academic Information Systems at Sebelas Maret University Indonesia

This study implements machine learning using the Naive Bayes algorithm to create a text classification in an engineering professional program information system. The methods used include text data collection, preprocessing, feature extraction, Naive Bayes model training, and evaluation using data testing. This study made a classification model to predict text categories with a test accuracy rate of 0.975 and a training accuracy of 0.967. This research contributes to the development of text classification in information systems and can be used as a basis for further study.

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

Simplifying Text Mining Activities: Scalable and Self-Tuning Methodology for Topic Detection and Characterization

In recent years, the number and heterogeneity of large scientific datasets have been growing steadily. Moreover, the analysis of these data collections is not a trivial task. There are many algorithms capable of analyzing large datasets, but parameters need to be set for each of them. Moreover, larger datasets also mean greater complexity. All this leads to the need to develop innovative, scalable, and parameter-free solutions. The goal of this research activity is to design and develop an automated data analysis engine that effectively and efficiently analyzes large collections of text data with minimal user intervention. Both parameter-free algorithms and self-assessment strategies have been proposed to suggest algorithms and specific parameter values for each step that characterizes the analysis pipeline. The proposed solutions have been tailored to text corpora characterized by variable term distributions and different document lengths. In particular, a new engine called ESCAPE (enhanced self-tuning characterization of document collections after parameter evaluation) has been designed and developed. ESCAPE integrates two different solutions for document clustering and topic modeling: the joint approach and the probabilistic approach. Both methods include ad hoc self-optimization strategies to configure the specific algorithm parameters. Moreover, novel visualization techniques and quality metrics have been integrated to analyze the performances of both approaches and to help domain experts interpret the discovered knowledge. Both approaches are able to correctly identify meaningful partitions of a given document corpus by grouping them according to topics.

Deep autoencoder-based fuzzy c-means for topic detection

Topic detection is a process for determining topics from a collection of textual data. One of the topic detection methods is a clustering-based method, which assumes that the centroids are topics. The clustering method has the advantage that it can process data with negative representations. Therefore, the clustering method allows a combination with a broader representation learning method. In this paper, we adopt deep learning for topic detection by using a deep autoencoder and fuzzy c-means called deep autoencoder-based fuzzy c-means (DFCM). The encoder of the autoencoder performs a lower-dimensional representation learning. Fuzzy c-means groups the lower-dimensional representation to identify the centroids. The autoencoder's decoder transforms back the centroids into the original representation to be interpreted as the topics. Our simulation shows that DFCM improves the coherence score of eigenspace-based fuzzy c-means (EFCM) and is comparable to the leading standard methods, i.e., nonnegative matrix factorization (NMF) or latent Dirichlet allocation (LDA).

Using Topic Modeling and Word Embedding for Topic Extraction in Twitter

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text's topic or theme. Topic analysis uses natural language processing (NLP) to break down human language into blocks (speech, words, sentences, context) so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions. The two most common approaches for topic analysis with machine learning are NLP topic modeling and NLP topic classification. Topic modeling faces several challenges: some are general to the NLP task (as extracting the context of document), and some are specific and are related to the nature (or properties) of the documents. One particular type of documents that raises several challenges are short-text documents that we find in Social Networks. First, we should note that with the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. However, despite their ubiquity, extracting topics from shorts texts remains a difficult task for several reasons. First, unlike traditional normal texts, short texts typically only include a few words. Therefore, directly applying traditional models on short texts will suffer from the severe data sparsity problem (i.e., the sparse word co-occurrence patterns in individual document). Second, the limited contexts make it more difficult to identify the senses of ambiguous words in short texts. Third, in general, the performance of the used machine learning models relies on labeled data. Unfortunately, due to their volume, labeling short-text from social networks remains a tedious and hard task. In this paper, we propose a model for extracting topics in short-texts, and more specifically in Twitter. The key feature of our proposal is the use of word embedding technique for topic modeling, and k-Means clustering for the semi-automatic annotation of tweets. In addition, and unlike most existing approaches, we assign in our model a set of topics to a tweet each with a confidence degree.

Semantics Analysis of Agricultural Experts’ Opinions for Crop Productivity through Machine Learning

ABSTRACT Semantic analysis is a particular technique, which is an interesting area of research that associates with Natural Language Processing (NLP), artificial intelligence, opinion mining, text clustering, and classification. Numerous text processing techniques are being used to find out sentiments from the comments, such as social media tweets, hoax, fiction, nonfiction, novels, books, movies, health care, and stock exchange. Agrarian experts’ opinions play a vital role in the agriculture sector that yields good crop productivity. This paper presents a descriptive analysis of agriculture experts’ opinions through machine learning methods based on textual data collection. The data has been collected by surveying various academia, research institute, and industry of Punjab, Pakistan. The impact of various agricultural inputs such as seed quality, soil quality, soil-intensive tillage, climate changes, water shortage, synthetic fertilizer, and precision technologies on crop productivity have been collected through questionnaires. This research provides a descriptive analysis of collected agrarians experts opinions to increase the crop yield by providing awareness regarding current agriculture inputs to farmers by using machine learning. The current research provides a cohesive expert guideline for improving crop productivity, useful for agricultural policymaking, and conveys adequate farmers’ knowledge. Consequently, the proposed method is an innovative way of discovering recommendations of agrarians through sentiment analysis in survey data using machine learning methods. Furthermore, to the best of our knowledge, agrarians experts opinions on enhancing crop productivity have been considered for the first time in Pakistan.

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear patterns from massive text data collections.

Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods.

Automatic Methods for Detecting Cultural Bias in Social Media (Based on Telegram’s Dialogs)

In this paper, we described and tested several ways to use machine learning in order to analyze large collections of text data from social networks (namely, public Telegram chat), retrieve relevant social or cultural information from them, and to visualize the results of the research. The proposed approach has an advantage to reveal hidden patterns of social, political or cultural behavior by being able to cover large amounts of data. It can complement the standard social surveys methodology. Automatic detecting cultural bias on the example of social media requires mastering methods for measuring and visualizing its different kinds, such as cultural shifts, specific national or group refractions, mutations, stereotypes. We argue that cultural bias is a result of nonrandom errors in thinking. It is based, firstly, on a person's understanding of himself and the world around him and, secondly, on the translation of this understanding into abstraction in the form of common misconceptions, ideologemes, narrative, slogans. In society the bias inevitably leads to the separation of one social group or subculture from another. Social networks (both classic and new formats, for example, messengers with public chat options) are the most active ground for the representation of this phenomenon. Since the discussion of sociopolitical and cultural contexts in the case of chats takes place in public, the participants of such a communicative act tend to get approval of the social group to which they are ideologically close. It is this phenomenon that allows us to form comparisons of the “friend - foe” type, which lead next to unconscious cultural shifts. Thus, mastering methods to identify properly cultural shifts is not only relevant but crucial for the intra- and intercultural communication, for controlling the level of aggressiveness of the society, understanding its mood. As helpful illustrations, readers will find semantic associations elicited by the words “freedom”, “democracy”, “Internet”; sociocultural analysis of several topical clusters (e.g. Россия, страна, Путин, русский, православный); visualization of semantic associations for the words “freedom”, “democracy”, “Internet”.