Text Analysis Techniques Research Articles

The interface between research funding and environmental policies in an emergent economy using neural topic modeling: Proposals for a research agenda

AbstractResearchers have been calling for further analysis of the relationships between academia and policy making. Since several studies are conducted with resources from public funding agencies, it is essential to assess whether governments have been supporting research that can contribute to future environmental policies and whether research has been supporting the elaboration of public policies. To fill these gaps, the objective of this study was to evaluate the similarities and gaps between research and environmental policies. A bibliometric analysis of studies funded by Chilean agencies and text analytics techniques were employed to analyze research and environmental policies. We used BERTopic, a topic modeling technique that generates coherent topic representations. Then, topics obtained from the two sets of documents (research and policies) were compared. This study found that sustainability, life‐cycle assessment, climate change, and ecosystem services are central research themes in the country. The topic modeling analysis showed that research has been concentrated on the areas of “Sustainable Fisheries and Aquaculture Management” and “Sustainable Bioenergy Production and Chemical Processes.” The topics most frequently present in the environmental policies were “Environmental Education for Children: Awareness and Care,” “Bird Conservation and Habitat Importance in Chile,” and “National Biodiversity Conservation and Sustainable Use Strategy.” The greatest gaps are observed in the clusters of “Sustainable Bioenergy Production and Chemical Processes” and “Copper Mining and Mineral Processing Techniques” (prevalent in research) and in “Anatomy and Physiology of Aquatic and Terrestrial Organisms” and “Biogeography and Taxonomy of Southern Hemisphere Insects” (prevalent in policy).

Word2Vec과 엔트로피를 활용한 연구동향 분석

To understand trends in a specific research field, qualitative literature analysis of major papers published in that field has traditionally been used. Recently, there has been an increasing effort to quantitatively analyze large volumes of literature using text analysis techniques. This study proposes an analysis method utilizing word embedding and entropy. The proposed method is applied to analyze literature in the field of Management Information Systems (MIS). For this purpose, 12,704 papers published in major MIS journals were collected. Word2Vec and annual keyword clustering were applied to 7,813 keywords extracted from these papers, and the research trends in the MIS field were analyzed through vector similarity analysis. Furthermore, research topics were visualized and classified according to their life cycles based on the rate of entropy change and average values. The method proposed in this study is applicable to various research fields and holds significance as a quantitative approach to analyzing large volumes of literature.

The Effect of Analysts’ Reports on Stock Liquidity: The Interaction of Ratings and Qualitative Indicators

We utilized a Python program to collect analysts’ reports on the constituents of the CSI 300 (China Securities Index). From these reports, we extracted analyst ratings, readability, and visualization using text analysis techniques. Additionally, a FinBERT model was employed to evaluate the tone and disagreement among analyst reports. Subsequently, the FGLS model was developed to explore the influence of analysts’ reports on stock liquidity. The analysis revealed a positive impact of analyst ratings on stock liquidity, with all qualitative indicators playing significant moderating roles. Specifically, there was a more pronounced response in stock liquidity to ratings when accompanied by higher readability, richer visualization, a more positive tone in the report, and lower disagreement. Our study highlights that investors consider both ratings and qualitative information from analyst reports, with the latter guiding investors on how to interpret ratings effectively. These findings have implications for investors, analysts, and regulators.

AlgoDM: algorithm to perform aspect-based sentiment analysis using IDistance matrix

Sentiment analysis is a method of analyzing data to identify its intent. It identifies the emotional tone of a text body. Aspect-based sentiment analysis is a text analysis technique that identifies the aspect and the sentiment associated with each aspect. Different organizations use aspect-based sentiment analysis to analyze opinions about a product, service, or idea. Traditional sentiment analysis methods analyze the complete text and assign a single sentiment label to it. They do not handle the tasks of aspect association, dealing with multiple aspects and inclusion of linguistic concepts together as a system. In this article, AlgoDM, an algorithm to perform aspect-based sentiment analysis is proposed. AlgoDM uses a novel concept of IDistance matrix to extract aspects, associate aspects with sentiment words, and determine the sentiment associated with each aspect. The IDistance matrix is constructed to calculate the distance between aspects and the words expressing the sentiment related to the aspect. It works at the sentence level and identifies the opinion expressed on each aspect appearing in the sentence. It also evaluates the overall sentiment expressed in the sentence. The proposed algorithm can perform sentiment analysis of any opinionated text.

Talking past each other: Institutional complexity in public projects

Large-scale public projects often experience delays and cost overruns, in part because they require extensive coordination between numerous organizations, both private and public. In this paper, we draw on the perspective of new institutionalism to explore differences between stakeholders in large-scale projects. We develop a topic modeling approach based on a state-of-the-art computational text analysis technique and apply it to a set of transcripts from parliamentary select committee hearings in the United Kingdom to uncover cultural-cognitive differences that reflect a variety of institutional logics. The application of this technique to four different projects reveals significant institutional differences between groups of actors. We argue that greater awareness of these institutional differences may support attempts to understand why large-scale projects experience such significant management challenges and how such challenges could be addressed. We use data for the period 2000–2019 from two different sectors, defence, and public transport.

IMPLEMENTATION OF LITERACY CULTURE IN VOCATIONAL SCHOOLS TO INCREASE STUDENTS' LEARNING MOTIVATION

This research aims to describe how literacy culture is implemented in vocational schools to increase student learning motivation. The research method uses participatory observation, where researchers directly observe daily activities in vocational schools related to the implementation of literacy culture. Respondents consisted of three teachers and 90 students from Computer Network and Telecommunications Engineering (TJKT) at SMKN 1 Gunungguruh Sukabumi. The research results show that by using a classical and independent approach, the school creates a learning environment that supports the development of students' literacy skills. The classical approach involves teachers in direct teaching and intensive guidance on reading, writing and text analysis techniques, while the independent approach provides opportunities for students for independent exploration in reading and writing. This combination not only improves students' literacy skills but also stimulates their creativity and increases motivation towards the learning process.

Leveraging Narrative Feedback in Programmatic Assessment: The Potential of Automated Text Analysis to Support Coaching and Decision-Making in Programmatic Assessment.

Current assessment approaches increasingly use narratives to support learning, coaching and high-stakes decision-making. Interpretation of narratives, however, can be challenging and time-consuming, potentially resulting in suboptimal or inadequate use of assessment data. Support for learners, coaches as well as decision-makers in the use and interpretation of these narratives therefore seems essential. We explored the utility of automated text analysis techniques to support interpretation of narrative assessment data, collected across 926 clinical assessments of 80 trainees, in an International Medical Graduates' licensing program in Australia. We employed topic modelling and sentiment analysis techniques to automatically identify predominant feedback themes as well as the sentiment polarity of feedback messages. We furthermore sought to examine the associations between feedback polarity, numerical performance scores, and overall judgments about task performance. Topic modelling yielded three distinctive feedback themes: Medical Skills, Knowledge, and Communication & Professionalism. The volume of feedback varied across topics and clinical settings, but assessors used more words when providing feedback to trainees who did not meet competence standards. Although sentiment polarity and performance scores did not seem to correlate at the level of single assessments, findings showed a strong positive correlation between the average performance scores and the average algorithmically assigned sentiment polarity. This study shows that use of automated text analysis techniques can pave the way for a more efficient, structured, and meaningful learning, coaching, and assessment experience for learners, coaches and decision-makers alike. When used appropriately, these techniques may facilitate more meaningful and in-depth conversations about assessment data, by supporting stakeholders in interpretation of large amounts of feedback. Future research is vital to fully unlock the potential of automated text analysis, to support meaningful integration into educational practices.

Framing Analysis of Increasing Fuel Prices News on cnnindonesia.com

This research discusses the framing of issues carried out by the online media cnnindonesia.com in reporting on the increase in fuel prices officially announced by President Joko Widodo. The aim of this research is to analyze the issue framing strategy used by cnnindonesia.com in packaging news about fuel price increases. This type of research is qualitative research using media text analysis techniques in the form of Robert N. Entman's framing model analysis of 18 news stories released by cnnindonesia.com on Saturday 3 September 2022. The results of the research show that the issue framing strategy used by cnnindonesia.com is in its reporting. provides a definition of the problem as a global economic problem and has an impact on social problems. An increase in the APBN is the cause of problems and places the government as an actor or source of problems. The moral decision was framed from the president's statement that it was difficult to make a decision regarding the increase in fuel prices and constructed the repressive actions of members of the National Police against demonstrators. Presenting a solution to the problem by framing a political narrative battle between the government and the opposition and explaining that the National Police Chief must be responsible for the repressive actions carried out by the police against students and workers in the action against the increase in fuel prices in all regions.

Experience in psychological counseling supported by artificial intelligence technology.

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

#Justice4Marielle: memory, datafication and justice among digital counterpublics that name Marielle Franco

This article seeks to examine the digital discursive practice of naming Marielle Franco on Twitter, mobilizing the perspectives and theoretical framework of Digital Humanities in the Global South, Data feminism and Datafication from Below. We also employ the methodology and theoretical framework of Digital Discourse Analysis and Critical Technocultural Discourse Analysis, integrating aspects of Performance Studies and Gender Studies to explore the relevance of memory and performance for contemporary digital counterpublics. We advance a technocultural discourse analysis of tweets that reference Marielle Franco between 2018 and 2023, in Spanish, English and Portuguese. We use a combination of techniques of computational textual analysis implemented in Wolfram Mathematica to study the chronological distribution of tweets; the emergence and relevance of hashtags; the users that were most frequently mentioned and the different topics they mobilised in social networks. Our conclusions point to the digital technocultural discourse on Marielle Franco as practices of memory and mourning that contribute to constitute a digital counterpublic in which naming, together with practices of datafication from below, are highly political, and serve not only to reassert collective identity but also to create a vocabulary to speak about the intersectional nature of oppression.

Automatic Algerian Sarcasm Detection from Texts and Images

In recent years, the number of Algerian Internet users has significantly increased, providing a valuable opportunity for collecting and utilizing opinions and sentiments expressed online. They now post not just texts but also images. However, to benefit from this wealth of information, it is crucial to address the challenge of sarcasm detection, which poses a limitation in sentiment analysis. Sarcasm often involves the use of non-literal and ambiguous language, making its detection complex. To enhance the quality and relevance of sentiment analysis, it is essential to develop effective methods for sarcasm detection. By overcoming this limitation, we can fully harness the expressed online opinions and benefit from their valuable insights for a better understanding of trends and sentiments among the Algerian public. In this work, our aim is to develop a comprehensive system that addresses sarcasm detection in Algerian dialect, encompassing both text and image analysis. We propose a hybrid approach that combines linguistic characteristics and machine learning techniques for text analysis. Additionally, for image analysis, we utilized the deep learning model VGG-19 for image classification, and employed the EasyOCR technique for Arabic text extraction. By integrating these approaches, we strive to create a robust system capable of detecting sarcasm in both textual and visual content in the Algerian dialect. Our system achieved an accuracy of 92.79% for the textual models and 89.28% for the visual model.

Qanā‘ah al-Ghazālī sebagai Landasan Stabilitas Mental: Pendekatan Holistik-Psikosufistik dengan Teknik Pernapasan 4-7-8

This article examines al-Ghazālī’s concept of qanā‘ah—contentment—as a pathway to mental health stability, offering insights into the wisdom and meaning behind applying Sufism and holistic therapeutic practices to address emotions such as anger, sadness, and anxiety. The 4-7-8 deep breathing technique is proposed as a complementary solution to these mental health challenges. The research adopts a psycho-sufistic approach to healing, focusing on the management of mental stability issues related to anger, anxiety, and sadness through the integration of Sufi qanā‘ah and the 4-7-8 breathing method, tailored for the modern era. A qualitative methodology with descriptive analysis is employed, drawing on library-based research. The data is sourced from various forms of documentation, including books, newspapers, transcripts, and other written materials, analyzed using text and discourse analysis techniques. The findings suggest that the application of psycho-sufistic qanā‘ah combined with the 4-7-8 breathing technique can effectively address mental stability issues, offering a means to alleviate anger, sadness, and anxiety in contemporary society. This holistic and Sufi-informed psychological approach aims to harmonize the heart and mind, fostering a more meaningful and fulfilling life for individuals in the modern age.

Annual report tone and divergence of opinion: evidence from textual analysis

ABSTRACT By utilizing web-crawling and text analysis techniques on unstructured big data (text sets), this study examines to what extent investors disagree with the sentiment conveyed in annual reports. The main empirical findings suggest that the tone of annual reports significantly influences investor opinions. Specifically, a negative tone in annual reports is associated with high levels of divergence among investors’ opinions, whereas a positive tone correlates with lower divergence. In the robustness tests, the results remain consistent after controlling for various factors. After we control for Management Discussion and Analysis (MD&A), both positive and negative tones in annual reports continue to be significant predictors of divergences in investor opinions. Additionally, after controlling for future earnings quality, future cash flows, and future earnings surprises, investors still present high/low divergence of opinion in response to a negative/positive tone in annual reports. Moreover, the robustness of our analysis is assessed by employing alternative sentiment analysis word lists.

General and Substantive Accountability in Nursing Home Services: Assessing Messages from the Demand Side and the Supply Side

ABSTRACT This study examines the extent to which general and substantive accountability is integrated into the language used by key actors involved in nursing home services. Particularly, we investigate the messages used by the supply side, which includes public and private organizations involved in residential care for older adults, and the demand side, which comprises organizations representing service beneficiaries. Moreover, we explore the alignment between the messages used by both sides of the accountability relationship. In the context of Spanish nursing homes, we analyzed a corpus of tweets by organizations from both sides of the accountability relationship, from one year before the outbreak of COVID-19 restrictions to after their implementation. Using text analysis techniques, we found that messages related to general and substantive accountability had a low priority before and after the outbreak. Public organizations were slightly more likely to employ general accountability terms than private organizations. This is particularly in non-crisis situations, although less frequently than organizations representing beneficiaries. Our analysis demonstrates a lack of convergence between the messaging on the supply and demand sides, indicating a communication breakdown between the two sides in the accountability relationship.

Managerial myopia and audit fees: Evidence from China

Following the upper echelons and time orientation theoretical frameworks, this study meticulously examines the effects of managerial myopia on audit fees utilizing data of Chinese A-share listed companies spanning from 2007 to 2022. Employing textual analysis and machine learning techniques on MD&A, we find that managerial myopia can significantly increase audit fees by intensifying financial constraints, diminishing accounting information comparability, and compromising internal control quality. However, this relationship is accentuated by analyst coverage but attenuated by digital transformation. Cross-sectionally, we document a particularly pronounced positive association between managerial myopia and audit fees within firms exhibiting higher corporate risk tolerance, diminished industrial competition and a lower degree of marketization. Collectively, this study further enriches corporate governance and audit fees literature, advancing valuable insights for regulators, accountants and auditors.

Do enterprises adopting digital finance exhibit higher values? Based on textual analysis

In this paper, we investigate whether enterprises adopting digital finance exhibit higher values. Based on the Management Discussion and Analysis (MD&A) sections of annual reports, we employ textual analysis and machine learning techniques to construct an indicator of the degree to which enterprises apply digital finance. We utilize panel data regression and mediation models based on data of Chinese A-share listed companies from 2016 to 2022 and explore the impact of this degree of digital finance application on enterprise value. We find that the degree to which enterprises apply digital finance elevates their values. The in-depth integration of digital technology and finance directly enhances enterprise value by reducing financing costs. Additionally, the effects are more evident among small-scale firms and enterprises in regions with lower marketization. However, in the face of the impact of the COVID-19 pandemic, the positive effects on enterprises are relatively low.

Sentimental analysis and prediction of socioeconomic disasters tweets by ML and regular expression

Sentiment analysis is a method of analyzing emotions and using text analysis techniques with natural language processing methods. Sentiment analysis uses data from various sources to identify the user’s attitude through different aspects. It is widely used for extracting opinions and recognizing sentiments, which helps Business organizations understand the user’s needs. This paper proposes a simple but compelling sentiment analysis method, showing the combined scores based on positive and negative words. Then, the tweets are categorized as Neutral, Negative, or Positive according to the scores. Sentiment analysis and opinion mining have grown significantly in the last decade. Different studies in this domain try to determine people’s feelings, opinions, and emotions about something or someone. The main objective of this analysis is to determine the sentiment of the review using a machine learning model and then compare the result with the manual review of the data. This would allow researchers to represent and analyze opinions objectively across different domains. A hybrid method that combines a supervised machine learning algorithm with natural language processing techniques is suggested for review analysis. This project aims to find the best model to predict the sentiment of the tweets on airlines. During the research process and considering various methods and variables that should be considered, we found that methods like naïve Bayes and random forest were not fully explored. The proposed system improves an effective and more feasible method for sentimental analysis using machine learning, multinomialNB, linear regression, and regular expression.

The 5 love languages of children (Chapman; Campbell, 2017): are its two main principles academically valid for qualitative research and teacher training?

This paper presents a qualitative analysis of seven quantitative academic studies on two main concepts from Gary Chapman's five love languages theory, the foundation of “The 5 love languages of children”. These studies were selected from a literature review conducted in 2021 through the State of Knowledge methodology. The texts were analyzed using the Discursive Textual Analysis technique. This article aims to show what has been studied on Chapman’s theory outside Brazil. Three of the studies confirmed the empirical validity of each of the five love languages as proposed in Chapman’s thesis. Additionally, two academic works empirically validated the primary love language hypothesis. Findings indicate both of Chapman’s claims as empirically valid concepts through quantitative research. Results suggest that these principles in the realm of childhood could be meaningful to qualitative research – on education and teacher training – to improve both teaching and learning processes.

The Green Chain: A Carbon Neutral Path from Climate Risk to Environmental, Social and Governance Performance

Global climate change is increasingly recognized as a critical factor influencing economic performance, thereby making the Environmental, Social, and Governance (ESG) performance of businesses a vital indicator of sustainability. In the context of China's "dual-carbon" objective, this study examines the relationship between climate transition risk and the ESG performance of listed Chinese companies. Utilizing text analysis techniques, this research quantifies the Management Discussion and Analysis (MD&A) sections of annual reports from 2014 to 2019 to extract information on climate transition risks. A multiple regression analysis is then applied to assess the impact of these risks on companies' ESG performance. The findings indicate that climate transition risk has a measurable effect on the ESG performance of enterprises, suggesting its role as an incentive for achieving carbon neutrality. Specifically, the analysis shows that climate transition risk is associated with improvements in certain aspects of ESG performance, including environmental stewardship, social responsibility, and governance quality. Additionally, the study reveals that non-state-owned enterprises, those with diversified business strategies, decentralized management structures, and audits conducted by Big 4 firms, tend to exhibit better ESG performance in the face of climate transition risks. These results demonstrate the nuanced impact of climate transition risk on ESG performance, particularly within the framework of China's carbon reduction goals, and highlight the importance of firm characteristics in moderating this relationship. This evidence provides a foundation for stakeholders, including enterprises, policymakers, and investors, to consider climate transition risks in their efforts to enhance ESG performance.

Does Surgical Robot Know What It’s Doing? Understanding Patient Fear and Anxiety

Robot-assisted surgery (RAS) is a fast-emerging area of medicine that offers numerous benefits compared to conventional open surgery. Nevertheless, certain patients may encounter fear and anxiety over robot-assisted surgery as a result of several circumstances. This study sought to examine the underlying factors contributing to fear and anxiety in patients who underwent robot-assisted surgery and to propose implications to alleviate such emotions. The study used a qualitative research design, collecting data via 25 semi-structured interviews with individuals who have undergone RAS for various ailments. The interviews were transcribed and examined using text analysis techniques. The primary factors contributing to fear and anxiety encompass a lack of knowledge and understanding regarding RAS, uncertainty about the outcome of surgery, loss of control, and exposure to strange instruments(robots). The data analysis revealed five primary themes: robot-assisted procedures, robotic arms, smaller incisions and recovery time, idea of technology role, and cutting edge. This study provides practical and valuable information about the adoption of robot-assisted surgery in surgical encounters. This study deepens our understanding of the factors that contribute to fear and anxiety resulting from interactions with surgical robots, while also broadening the scope of research on surgical robots. The healthcare providers can perform preoperative psychological evaluations to identify patients with a heightened susceptibility to anxiety and fear. Moreover, providing psychological assistance or therapy, and instructing patients on relaxation techniques and mindfulness practices help them to effectively cope with anxiety prior to and following surgical procedures.