Text Analysis Applications Research Articles

Text analytics for co-creation in public sector organizations: a literature review-based research framework

The public sector faces considerable challenges that stem from increasing external and internal demands, the need for diverse and complex services, and citizens’ lack of satisfaction and trust in public sector organisations (PSOs). An alternative to traditional public service delivery is the co-creation of public services. Data analytics has been fueled by the availability of immense amounts of data, including textual data, and techniques to analyze data, so it has immense potential to foster data-driven solutions for the public sector. In the paper, we systematically review the existing literature on the application of Text Analytics (TA) techniques on textual data that can support public service co-creation. In this review, we identify the TA techniques, the public services and the co-creation phase they support, as well as envisioned public values for the stakeholder groups. On the basis of the analysis, we develop a Research Framework that helps to structure the TA-enabled co-creation process in PSOs, increases awareness among public sector organizations and stakeholders on the significant potential of TA in creating value, and provides scholars with some avenues for further research.

Reviving functionalism: Applying translation-oriented text analysis to synthetic text (editing)

This paper investigates the potential application of translation-relevant text analysis. Rooted in the functionalist approach and Skopos theory, the translation-relevant text analysis is seen as instrumental in translation education for assessing the quality of synthetic text output for translation purposes (MT and/or AI) and improving (post-)editing performance relating to said output. Despite recent advancements in machine translation and the partially self-professed potential of AI models, accurately conveying the intended function of the target text remains a challenge in professional translation practices. This paper argues that salvaging functionalist theory can benefit both translator education and, ultimately, professional translation practices. To support this argument, three use cases are presented in which the key principles of functionalism and Skopos theory are applied to develop critical text analysis skills necessary for handling and editing synthetic text. Keywords: Functionalism, Translation Technology, Machine Translation, Generative Artificial Intelligence, Post-editing

Me-LLaMA: Medical Foundation Large Language Models for Comprehensive Text Analysis and Beyond.

Recent advancements in large language models (LLMs) like ChatGPT and LLaMA have shown significant potential in medical applications, but their effectiveness is limited by a lack of specialized medical knowledge due to general-domain training. In this study, we developed Me-LLaMA, a new family of open-source medical LLMs that uniquely integrate extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA comprises foundation models (Me-LLaMA 13B and 70B) and their chat-enhanced versions, developed through comprehensive continual pretraining and instruction tuning of LLaMA2 models using both biomedical literature and clinical notes. Me-LLaMA utilized the largest and most comprehensive medical data, including 129B pre-training tokens and 214K instruction tuning samples from diverse biomedical and clinical data sources. Training the 70B models required substantial computational resources, exceeding 100,000 A100 GPU hours. We applied Me-LLaMA to six medical text analysis tasks and evaluated its performance on 12 benchmark datasets. To further assess Me-LLaMA's potential clinical utility, we evaluated its performance on complex clinical case diagnosis compared with other commercial LLMs, using both automatic and human evaluations. Me-LLaMA models outperform LLaMA, and other existing open-source medical LLMs in both zero-shot and supervised learning settings for most text analysis tasks. With task-specific instruction tuning, Me-LLaMA models also surpass leading commercial LLMs, outperforming ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. Moreover, Me-LLaMA's performance is comparable to ChatGPT and GPT-4 for diagnosing complex clinical cases. Our findings underscore combining domain-specific continual pretraining with instruction tuning is essential for developing effective domain-specific large language models in healthcare, significantly enhancing performance across diverse medical text analysis tasks and applications. By publicly releasing our models and resources under appropriate user agreements, we aim to foster innovation and facilitate advancements in medical AI, benefiting researchers and practitioners within the community.

Text Analysis of Archival Finding Aids

Archival repositories must be strategic and selective in deciding what collections they will acquire and steward. Careful collection stewards balance many factors, including ongoing resource needs and future research use. They ensure new acquisitions build upon existing topical strengths in the repository’s holdings and reassess these existing strengths regularly through multiple lenses. In this study, we examine the suitability of text analysis as a method for analyzing collection scope strengths across a repository’s physical archival holdings. We apply a tool for text analysis called Leximancer to analyze a corpus of archival finding aids to explore topical coverage. Leximancer results were highly aligned with the baseline subject heading analysis that we performed, but the concepts, themes, and co-occurring topic pairs surfaced by Leximancer suggest areas of collection strength and potential focus for new acquisitions. We discuss the potential applications of text analysis for internal library use including collection development, as well as potential implications for wider description, discovery, and access. Text analysis can accurately surface topical strengths and directly lead to insights that can inform future acquisition decisions and archival collection development policies.

Semantic clustering method using integration of advanced LDA algorithm and BERT algorithm

The subject of the study is an in-depth semantic data analysis based on the modification of the Latent Dirichlet Allocation (LDA) methodology and its integration with the bidirectional encoding representation of transformers (BERT). Relevance. Latent Dirichlet Allocation (LDA) is a fundamental topic modeling technique that is widely used in a variety of text analysis applications. Although its usefulness is widely recognized, traditional LDA models often face limitations, such as a rigid distribution of topics and inadequate representation of semantic nuances inherent in natural language. The purpose and main idea of the study is to improve the adequacy and accuracy of semantic analysis by improving the basic LDA mechanism that integrates adaptive Dirichlet priorities and exploits the deep semantic capabilities of BERT embeddings. Research methods: 1) selection of textual datasets; 2) data preprocessing steps; 3) improvement of the LDA algorithm; 4) integration with BERT Embeddings; 5) comparative analysis. Research objectives: 1) theoretical substantiation of LDA modification; 2) implementation of integration with BERT; 3) evaluation of the method efficiency; 4) comparative analysis; 5) development of an architectural solution. The results of the research are that, first of all, the theoretical foundations of both the standard and modified LDA models are outlined, and their extended formula is presented in detail. Through a series of experiments on text datasets characterized by different emotional states, we emphasize the key advantages of the proposed approach. Based on a comparative analysis of such indicators as intra- and inter-cluster distances and silhouette coefficient, we prove the increased coherence, interpretability, and adaptability of the modified LDA model. An architectural solution for implementing the method is proposed. Conclusions. The empirical results indicate a significant improvement in the detection of subtle complexities and thematic structures in textual data, which is a step in the evolutionary development of thematic modeling methodologies. In addition, the results of the research not only open up the possibility of applying LDA to more complex linguistic scenarios, but also outline ways to further improve them for unsupervised text analysis.

CEO’s tendency to narcissism and optimism: The impact on a company’s debt burden before and during the COVID-19 pandemic

Goal: to assess the impact of the level of the CEO’s optimism and narcissism on the capital structure of companies in the developed and emerging markets before and during the COVID-19 pandemic. Metodology: the study is based on 100 Western companies from the “Forbes Global 2000” list and 50 leading Russian companies in terms of revenue and capitalization. The assessment of narcissism and optimism was carried out by application of text analysis based on the bag of words and the pronoun counting method. Using the Anglin dictionary, four manifestations of narcissism were assessed — superiority (manifestation of superiority), vanity (vanity), exhibitionism (narcissism), self-sufficiency (arrogance). The hypotheses about the relationship between a CEO’s personality characteristics (narcissism and optimism) and the company’s capital structure were tested by using panel regression with fixed effects. Findings: the study revealed that the influence of the behavioral characteristics of a CEO increased during the crisis period (pandemic), which may indicate the tendency of managers to act less rationally and more impulsively; the result was confirmed for both domestic and foreign managers. The interplay between narcissism and optimism has a similar trend with the level of debt burden in Russian and international companies, i.e. for decision-making, the personal characteristics of management are more important than regional business specifics. Originality and contribution of the authors: the study contributes to the literature on behavioral finance by assessing the impact of a CEO’s narcissism and optimism in the context of the COVID-19 pandemic and expanding the existing work towards the analysis of crisis situations as well as behavioral finance in different economic and sociocultural areas. The paper proposes text analysis to assess personal characteristics of CEOs.

Формирование лексического модуля прикладной онтологии для её обучения

The complexity of developing ontologies is associated with their learning, which involves the automation of solving problems related to the extraction of ontological concepts and relationships from natural language texts. For a created ontology used in an information system, there will most likely be a need to expand its conceptual system due to the complication or change in data processing. Subsequent application of text analysis to discover new concepts and in-corporate them into the ontology, that is, linking them with existing concepts, will require identifying the latter in sen-tences of natural language texts. This paper considers the problem of automating the formation of the lexical module of applied ontology, which includes formalized representations of ontological concepts in natural language texts. A brief review of existing works devoted to the use of lexical information about ontology components in solving problems related to the analysis of textual data is presented. The ways of using the OntoLex-Lemon model for determining the structure of lexical representations of ontology concepts are considered. A procedure is proposed for the formation of lexical representations based on the analysis of texts in the subject area, taking into account the case when concepts have names consisting of several words. The results of applying the obtained module for the automatic formation of a training set of a neural network language model used in the ontology learning task for discovering new concepts in the corpus of subject texts are presented.

Comparison of NB and SVM in Sentiment Analysis of Cyberbullying using Feature Selection

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.

Impact of Maker Movement on the Urban Resilience Development: Assessment Methodology and Analysis of EU Research and Innovation Projects

Cities are the engine of human development, and increasing urban sustainability is crucial to ensure human prosperity. The development of smart cities generally increases the sustainability of the cities. However, technical and environmental aspects are generally developed in smart cities neglecting socio-economic dimensions. The urban resilience concept includes the complex interactions of environmental, economic, and societal pillars. In this context, the emerging maker movement proposes an economic paradigm shift, with the interaction of humans and technology at the center of urban evolution. This paper proposes a multi-criteria methodology to define and assess the main characteristics of the resilient approach of the projects involving maker practices applied to urban development. The proposed methodology is based on the application of computer-assisted qualitative text analysis and a subsequent classification according to 12 indicators (community and urban efficiency, co-creation and professional, making sense and problem-solving, network and site-specific, implementation and optimization, sustainability-oriented and market-oriented) that define different dimensions of a bottom-up project’s resilient approach in three main key principles: inclusiveness, complexity, and durability. The method has been tested in 94 EU-funded projects. This analysis reveals the evolution and orientation of EU-funded projects from economic, technical, and social perspectives. Specifically, the patterns of remediation of non-participatory practices, the weak presence of open innovation initiatives, and the development of activities focusing on co-creation as a participatory tool. The applied methodology could be subsequently implemented at different scales and integrated with LCA in order to evaluate the sustainability of bottom-up projects toward urban development.

Text Summarization and Conversion of Speech to Text

Abstract: This article describes the fusion of recurrent neural networks and deep learning algorithms for text summarization systems and analysis of the text learning process. Next, the text analytics learning model is summarized. In addition, applications of deep learning-based text analysis are also introduced. Language is the most important part of communication between people. Although there are many ways to express our thoughts and feelings, language is considered the most important medium of communication. Speech recognition is the process by which machines recognize different people's voices based on specific words and phrases. End-to-end deep learning techniques can be used to identify and simplify spatial representations of text data and semantic information. This study considers text analysis based on deep learning

The Automatization of Social Media Communication

This research paper compiles and evaluates several studies that have a particular interest in social media analysis and its applications. Studies on evaluating the impact of movies and television shows on social media platforms, using semantic knowledge graphs to analyze Covid-19 news articles and identify fake news on social media, forecasting social media data using machine learning, and the evolution of the power of central nodes in a Twitter social network are all covered in this article. The significance of sentiment analysis and opinion mining in social media, analysis of social media-based profiles, and mining serendipitous drug use from social media using machine learning are all topics covered in the paper. The paper also emphasizes the application of social media text analysis for a high-frequency-link DC transformer based on switched capacitors for medium-voltage DC power distribution applications, and urban region mining services. The research paper offers details on the state of social media analysis right now and some potential uses for it.

Application of text mining technology for comparative analysis of trends in the labor market

Object: The main purpose of the article is to analyze the employment of the population in the post-Soviet space in the context of world events to identify positive and negative trends in employment, as well as characteristic trajectories of development directions.Methods: Modern methods of word processing, in particular text mining, word cloud were used.Findings: The rapid development of modern technologies, Internet applications is accompanied by the generation of large amounts of data, the timely processing of which is today one of the main problems in various spheres of life - social, economic, political, and others. In solving this global problem, modern methods of processing text information, the so-called text mining technologies, come to the rescue. These tools allow to increase the efficiency of solving problems of different levels. The algorithms embedded in the text mining technology reveal the basic concepts of the text, the content and the relationship between them.The integration of modern text mining systems and the R-Studio programming language makes it possible to conduct research in the field of text analysis and processing. These systems, using statistical methods, process the rating of news documents, materials of scientific documents, blogs, tweets, emails, advertisements and other information. The main task of text analysis is to get a clear idea about the topics of interest, to extract important information. The analysis of text documents by text mining methods is carried out in several stages: information search, text preprocessing, extraction of the required information, application of text methods, analysis and interpretation of the obtained results. For the analysis of texts, articles in Russian in PDF format were selected, including information on trends in the labor market and employment for 1995–2020.Conclusions: over the past 20 years there have been significant changes in the labor market and employment. Text analysis technologies made it possible to reveal that during the study period, the labor market issues of unemployment, employment, employment transformation, the emergence of new forms of employment, social and gender problems, and others raised.

Emerging Associates of the Circular Economy: Analysing Interactions and Trends by a Mixed Methods Systematic Review

Circular Economy (CE) frameworks augmented multi-dimensional research, including definitions, principles, indicators, history, limitations, enablers, and allied issues. Rethinking the designing strategies requires the exploration of CE associates and their functioning as companions for amplification through multiple suggestions, highlighting the interactions with the overall CE system towards sustainability. This paper focuses on how CE literature concentrates on premeditated areas and solution approaches. A lack of deterministic manifestations critically limits the interpretation and application of CE concepts. The paper contributes to the CE theoretical and practical understanding by for the first time outlining the CE associates, their connotation, progression, positioning in architecture, and their current state. These associates support individualization of a CE concept. The coherently applied methodology attempts to reduce the knowledge gap by following a systematic quantitative literature review process to screen 76 articles and 122 CE definitions, referencing one decade of literature. Application of comprehensive morphological mining facilitated the extraction of CE associates and later analysis. On application of Bibliometric Analysis, Text Analysis, Semantic Analysis, and Relation Mapping to the literature, 49 associates emerged. They were further validated by application of Content Analysis and Weighted Average Priority Ranking on extracted article content and definitions for deriving the most prominent associates. Subsequently, Association Rules Data Mining and K-Means Clustering were deployed in exploring the relations, ranking, and significance of CE associates. The findings consistently state that business actions (virtualize, exchange); sustainability pillars (society); and R strategies (refuse, refill, and repurpose) are unexplored CE associates, whereas Business, Economics, Environment, Measurement, Product, Industry, Resource, System, Waste, and Recycle are the most explored associates of the CE ecosystem.

The model of perceptual quality on private brands: Application of text analysis

This study takes the customers of RT-Mart, Auchan and Wal-Mart as the research objects, and explores the influence of product type on consumers' perception of private brand products. A total of 30 customers were interviewed and researched. The methodology adopted the application of text analysis, and the data analysis was carried out with grounded theory. The context is summarized, and the insights of theoretical propositions are put forward. Finally, The Model of Perceptual Quality on Private Brands is established, which provides academic reference indicators and the direction for managers to operate well.

The model of perceptual quality on private brands: Application of text analysis

The model of perceptual quality on private brands: Application of text analysis

Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis

AbstractSentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document’s stance with respect to a given target—how positively or negatively it frames a specific idea, individual, or group—as this reflects the author’s underlying political attitudes. In this paper, we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document’s sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.

Application of sentence-level text analysis: The role of emotion in an experimental learning intervention

This registered study aimed at testing the role of emotion in the intervention effect of an experimental intervention study in academic settings. Previous analyses of the National Study of the Learning Mindset (Yeager et al., 2019) showed that in a randomized controlled trial, high school students who were given the growth mindset intervention had, on average higher GPA than did students in the control condition. Previous analyses also showed that school achievement levels moderated the intervention effect. This study applied a sentence-level text analysis strategy to detect participants' attentional focus in five emotional dimensions (valence, arousal, dominance/control, approach-avoidant, and uncertainty) across three writing prompts students wrote during the intervention. Linear mixed models were conducted to test if emotional dimension scores computed using the text analysis predicted a higher intervention effect (i.e., higher post-intervention GPA given pre-intervention GPA). The moderating role of school achievement levels was also examined. The results of this study have implications on the possibility of applying text analysis strategies on open-ended questions in interventions or experimental studies to examine the role of the emotion-attentional focus of participants during intervention or experimental studies on the intervention or experimental outcomes, especially those that are conducted in academic settings.

The World of Topic Modeling in R

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.

Methodology for the Application of Philological Text Analysis in Teaching the Legal Language in the Professional Training of Specialists for the Judicial System

Methodology for the Application of Philological Text Analysis in Teaching the Legal Language in the Professional Training of Specialists for the Judicial System

A Brief Survey on Text Analytics Methods and Applications

About 80% organizational data are present in the unstructured (Text) format. E-mails, Social media, notes, and wide variety of different types of documents in text formats are present, but all these data are not got importance and analyzed in meaningful ways. It has been observed that information workers spend their significant time (up to one third) to locating this information and trying to make sense of it. Text analytics (TA) is the process which analyzed all these available unstructured text information and converts it into useful information which helps the organization significantly in their business processes. In this paper we have discussed the business values, methods of text analytics, and business application of text analytics.