Text Analysis Techniques Research Articles

Penerapan Konsep Kesehatan Jiwa Imam Al-Ghazali Dalam Bimbingan Dan Konseling Islam

Mental health is a vital development factor and a national goal. Citizens of a country who are mentally healthy are expected to be able to contribute actively to the society in which they live. In an ideal state of mental health, a person is not only physically, mentally, spiritually and socially healthy, but also has the potential to lead a fruitful life. However, in reality, many different mental health problems exist in life. This study aims to discuss mental health in the perspective of the teachings of Imam Al-Ghazali. This investigation is a literary analysis based on textual analysis techniques using the literature review method. Mental health is seen as a model that includes adherence to one's religious beliefs, freedom from mental illness, development of a mature worldview, development of positive social relationships, and the desire for material and spiritual success. This article examines various approaches to mental health inspired by Imam Al-Ghazali that can be adapted to individual needs for guidance and support. The benefits of incorporating mental health concepts into training and counseling are explored in detail.

The macroeconomic effects of oil price uncertainty

We study the impact of oil price uncertainty (OPU) on macroeconomic activity. We construct a new measure of OPU by extracting information on oil markets from 50 newspapers around the world using textual analysis techniques. Our index spikes around important historical events, such as the oil price shock of the 1970s and the supply glut of the 1980s. The effects of OPU on macroeconomic activity are captured using Bayesian vector autoregressive techniques. OPU is an important source of macroeconomic fluctuations in the US, with industrial production displaying a maximum estimated drop of 0.34% almost 10 months after impact. These negative effects are sharpened when monetary policy is constrained by the Zero-Lower Bound. OPU exerts an economically meaningful impact on global economic conditions and affects economic activity in other industrialized countries.

A Multi Document Summarization of Learning Materials using Bigram Embedding Technique and Integer Linear Programming

In the present era of the Internet, teachers and learners are heavily inclined to use e-learning systems for an efficient learning process. Due to the proliferation of educational text contents in these e-learning systems, the need for incorporating advanced text analysis tools and techniques are becoming inevitable. Multi Document Summarization (MDS) is a technique for producing concise summaries from a collection of related text documents. The usage of MDS in the context of e-learning is more promising for providing summaries for learning materials which helps students and teachers to focus on key concepts of the learning materials. In this paper a semantic approach towards the learning material summarization is proposed based on bigram embedding and ILP technique. This approach considers bigram as the basic meaningful semantic unit of the sentences to understand and summarize documents. Embedding techniques are employed to learn the vector representation of phrases to semantically identify similar phrases to reduce the redundancy and improving coherence. Using ILP technique, the summaries were generated by selecting important sentences while reducing the redundancy using phrase vectors. Experimental results on newly created educational dataset (EduSumm) shows better performance compared to the baseline systems.

Research on Social Service Effectiveness Evaluation for Urban Blue Spaces—A Case Study of the Huangpu River Core Section in Shanghai

Urban blue spaces (UBSs) hold significant value in terms of public health, tourism economy, and residents’ well-being. The Huangpu River in Shanghai, renowned as a global urban blue space, currently faces challenges such as unequal service capacity across sections and varying levels of spatial vitality. This study incorporates the concept of “service effectiveness” into public space evaluation. Drawing inspiration from the 4E (economics, efficiency, equity, and effectiveness) principles of effectiveness evaluation, a social service effectiveness evaluation system is constructed to measure service efficiency and effect. Through the literature research, 6 primary indicators and 12 secondary indicators are set to investigate the utilization rate and realization effect of the UBS. The evaluation system utilizes field surveys, text analysis, and remote-sensing techniques to collect relevant data. Through standardized calculations, different aspects of the indicators are integrated into a single evaluation criterion. The “overall effectiveness index” and the “efficiency–effect balance index” is introduced to quantitatively analyze the overall effectiveness characteristics, including spatial characteristics and time-varying characteristics, as well as efficiency–effect balance and imbalances. The evaluation located three low-effectiveness sections and three imbalanced sections at the north and south ends of the core section of the Huangpu River. The influence factors of effectiveness are analyzed through correlation test and literature studies, mainly including the urban hinterland, service facilities, environmental quality, and management publicity factors. This study aims to provide research ideas and methods for waterfront area planning and city-refined management.

Corporate social and environmental reporting in the mining sector: seeking pragmatic and moral forms of legitimacy?

PurposeThe study examines the social and environmental responsibility indicators disclosed by three International Council on Mining and Metals (ICMM) corporate mining members in their social and environmental reporting (SER) from 2006 to 2014. To achieve this aim, the author limits the data two years before (i.e. from 2006 to 2007) and six years after (i.e. from 2009 to 2014) the implementation of the Sustainable Development Framework in the mining sector in 2008.Design/methodology/approachUsing the techniques of content analysis and interpretive textual analysis, this study examines 27 social and environmental responsibility reports published between 2006 and 2014 by three ICMM corporate mining members. The study develops a disclosure index based on the earlier work of Hackston and Milne (1996), together with other disclosure items suggested in the extant literature and considered appropriate for this work. The disclosure index for this study comprised six disclosure categories (“employee”, “environment”, “community involvement”, “energy”, “governance” and “general”). In each of the six disclosure categories, only 10 disclosure items were chosen and that results in 60 disclosure items.FindingsA total of 830 out of a maximum of 1,620 social and environmental responsibility indicators, representing 51% (168 employees, 151 environmental, 145 community involvement, 128 energy, 127 governance and 111 general) were identified and examined in company SER. The study showed that the sample companies relied on multiple strategies for managing pragmatic legitimacy and moral legitimacy via disclosures. Such practices raise questions regarding company-specific disclosure policies and their possible links to the quality/quantity of their disclosures. The findings suggest that managers of mining companies may opt for “cherry-picking” and/or capitalise on events for reporting purposes as well as refocus on company-specific issues of priority in their disclosures. While such practices may appear appropriate and/or timely to meet stakeholders’ needs and interests, they may work against the development of comprehensive reports due to the multiple strategies adopted to manage pragmatic and moral legitimacy.Research limitations/implicationsA limitation of this research is that the author relied on self-reported corporate disclosures, as opposed to verifying the activities associated with the claims by the sample mining companies.Practical implicationsThe findings from this research will help future social and environmental accounting researchers to operationalise Suchman’s typology of legitimacy in other contexts.Social implicationsWith growing large-scale mining activity, potential social and environmental footprints are obviously far from being socially acceptable. Powerful and legitimacy-conferring stakeholders are likely to disapprove such mining activity and reconsider their support, which may threaten the survival of the mining company and also create a legitimacy threat for the whole mining industry.Originality/valueThis study innovates by focusing on Suchman’s (1995) typology of legitimacy framework to interpret SER in an industry characterised by potential social and environmental footprints – the mining industry.

A comprehensive dual-layer architecture for phishing and spam email detection

The widespread use of email communication for sharing personal, professional, and financial data has rendered it vulnerable to cyber-attacks. Detecting untrustworthy emails with minimal errors is crucial to counter these threats. This research paper focuses on classifying spam and phishing emails, commonly employed to steal confidential information by impersonating legitimate sources. The scale of such attacks is alarmingly high, causing substantial financial losses across sectors like banking, healthcare, technology, and other businesses. This research aims to classify both spam and phishing emails, addressing the limitations of existing studies that only focus on one type and consider either the email body or content for feature selection. This research incorporates features from both the email body and content during model training. The authors propose a novel approach that ensures highly accurate classification while effectively handling the common issue of data imbalance in email phishing and spam classification. The approach utilizes a dual-layer architecture, with each layer containing a trained or pre-trained model that classifies data instances into their respective classes. Layer 1 classifies the phishing class, while Layer 2 classifies the spam class. Building upon the proven effectiveness of deep learning techniques for text classification and analysis, the proposed architecture employs models like Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN). Experimental evaluation demonstrates the approach’s remarkable accuracy, recall, precision, and F1-score, achieving 99.51%, 99.68%, 99.5%, and 99.52%, respectively. This signifies its high efficacy in detecting and classifying malicious emails with minimal errors, thus holding great promise in enhancing system security against cyber-attacks in email communication.

Are the answers all out there? Investigating citizen information requests in the haze of bureaucratic responsiveness

AbstractUnder freedom of information (FOI) regulations, public officials are pressured to grant citizens access to public information by responding to citizen information requests. However, despite FOI regulations, information requests are treated with varying bureaucratic attention, resulting in a high number of ignored or overdue requests by public organizations. Focusing on this bureaucratic discrimination, this study aims to explain the determinants of varying bureaucratic responsiveness to citizen information requests. Responsiveness in this case is therefore either a successful response to the request, a refusal to respond to the request, or no response at all (i.e., the request is ignored). By drawing on public accountability, and thus a citizen‐driven model of bureaucratic responsiveness, we shed light on the human aspect behind responsiveness to information requests. This research argues that the request's topic as an accountability‐seeking indicator and the communication tone as an indicator for underlying emotions influence responsiveness. The results from applying text mining and text analysis techniques, such as topic modeling and sentiment analysis, on over 100,000 citizen information requests filed via a German online FOI platform support these assumptions.

Financial constraints and political catering disclosures of non‐state‐owned firms: Evidence from textual analysis

AbstractUsing textual analysis techniques, we construct a novel measure to quantify the level of political information in forward‐looking disclosures of privately controlled firms. Our results show that private firms with serious financial constraints are more likely to reveal more political information in forward‐looking disclosures. Plausible mechanisms driving our findings are firms' high growth, under‐investment, and weaker political connections, suggesting that firms cater to governments in the expectation of more financing resources. Finally, we show the economic consequences of political catering strategy on corporate performance.

Information Extraction and Passport Recognition

Abstract: The passport recognition and information extraction project aims to develop a software system that can automatically recognize and extract key information from passports. The system will utilize computer vision techniques to process passport images and extract important data such as the passport holder's name, date of birth, passport number, and expiration date. The extracted information will then be used to populate a database or other software systems for further processing. The project will involve the use of deep learning algorithms for image recognition and feature extraction, as well as natural language processing techniques for text extraction and analysis. The system will be designed to operate accurately and efficiently across a range of different passport types and will be suitable for use in a variety of applications, including border control, identity verification, and travel management.

Misyari Rasyid Alafasyi’s “Qolbi Muhammad” Song Lyrics: Michael Riffaterre’s Semiotic Study

The purpose of this study is not only to reveal the heuristic meaning in the lyrics of the song "Qolbi Muhammad" by Misyari Rasyid Alafasyi, but also to reveal the hermeneutic meaning in the song "Qolbi Muhammad" by Misyari Rasyid Alafasyi. This research is a qualitative descriptive research. The primary data sources in this study were the lyrics of the song "Qolbi Muhammad" by Misyari Rasyid Alafasyi, and the secondary data sources in this study were scientific papers, books, and other references. Data collection techniques using text analysis techniques and literature study. Data analysis techniques consist of data reduction, data presentation, and drawing conclusions. The findings of this study are based on the heuristic meaning of the song "Qolbi Muhammad" by Misyari Rasyid Alafasyi, the researchers found the results of lexical meaning in the form of advice from a Munsyid, namely Misyari Rasyid Alafasyi to mankind in general and to Muslims in particular to always be on His path that has been exemplified by the Prophet Muhammad. so that we are always in the protection of Allah swt. As for the results of the hermeneutic meaning in the song, the researchers found a matrix or essence of the song, namely the indicator of the existence of a servant is his faith and devotion to Allah swt and how he relates to the Prophet Muhammad. This means that a servant must always increase his faith and devotion to Allah swt by following the path or teachings brought by the noblest role model, namely the Prophet Muhammad.

Strict Party Governance and economic development: Evidence from China

China's economy has shifted from a period of high-speed growth to a new era of high-quality development and urges for further deepening the reform of government, market and the self-revolution of the Communist Party of China (CPC), among which Strict Party Governance (SPG) is an essential way. Based on the reports, communiqués, resolutions, editorials of the CPC, we use text-analysis techniques to construct a SPG Index and study the relationship between SPG and China's economic development (i.e., economic growth, pollution, innovation and income gap). Empirical results show that SPG has significantly improved China's economic development, and the effect is more pronounced in regions with higher proportion of the state-owned economy and higher level of fiscal decentralization. In terms of channel analysis, we find that the government efficiency, the degree of integrity of officials, and the marketization level are important channels for SPG in affecting economic development. Further analysis shows that SPG plays a more significant role during the period of anti-corruption and areas with weaker legal protection. Results reveal that SPG is a key factor in China's economic transition and development.

Herding the Literature: Trends in Large Mammalian Herbivore Grazing and Foraging Ecology Research over the Past Three Decades

Large herbivores are often considered keystone species, driving the structure composition and dynamics within ecosystems, particularly for rangelands, which are often defined in terms of their productive capacity for livestock and game products. Grazing and foraging ecological research have been instrumental in developing a theoretical and empirical understanding of the role these herbivores play in natural and agricultural systems. We used a variety of text analysis techniques (e.g., topic modelling, geoparsing, taxonomic entity extraction) to assess key questions in the academic literature on grazing and foraging ecology research. These relate to understanding of the spatial and temporal trends and the main topics encapsulated within this research. We identified clear and distinguishable topics of research activity that spanned across methodological and analytical fields and subject matter. The subject matter topics related to specific areas of research (e.g., seed germination and dispersal), systems and modeling (e.g., modeling grazing systems), and management (e.g., rangeland management). Our analyses showed how the prevalence of these topics in the literature changed over time. Rangelands were the most common biome for research, with deer (Cervus and Odocoileus) as the most mentioned genera. The United States produced the most research, but China's output has grown over time. The research demonstrates that grazing and foraging ecology of ungulates (livestock and wildlife) is increasing; is highly dynamic in topic, space, and time; and that rangelands are, and have been, important in the development of research in the fields. Trends suggest that rangelands and rangeland management will continue to be important areas of research with modeling and greenhouse gas emissions growing in prevalence, particularly as China increases its contribution to the corpus.

A Study on Convergence of Text Analysis Techniques for Social Science Research in the Age of Big Data

In this paper, we do not only propose appropriate application methods of the semantic network analysis technique, topic modeling technique and text clustering technique, which are techniques that can analyze text document sets but also the convergence application methods between each techniques. Appropriate application methods for the text analysis techniques were presented by dividing exploratory analysis process and modeling process for analyzing the text document set. Semantic network analysis would be not only suitable for exploratory analysis of the entire text document set, but also applicable to performance improvement of topic modeling and text clustering. The meaning of the topic could be interpreted more clearly by applying semantic network analysis secondarily to the document set of each topic derived by topic modeling. It can be used for text cluster interpretation by applying semantic network analysis to the document set of each cluster, which is the result of text cluster analysis. When deriving an appropriate number of topics, a more accurate number of topics could be derived by adding a silhouette analysis along with the degree of perplexity and coherence. New social science research can be expected to be revitalized by enabling more accurate analysis of online text documents using the method proposed in this paper.

Systematic Objectification of Eastern Women in Western Society: A Case Study of ‘The Optimist’ by Bina Shah

Patriarchy is deeply rooted in all societies of the world and many sociological, political and religious reasons are behind this ideology. This was not highlighted by western writers in the past but it exists in the western modern world is also as presented by Loius Tyson (2006). He has claimed that women are kept oppressed economically, politically, socially and psychologically by patriarchy, moreover, women are considered as the ‘other’, therefore objectified and marginalized. The writings of eastern writers like the short story ‘The Optimist’ written by Bina Shah verifies the claims of Tyson. The narrated story proves that oppression of women is not the issue of eastern western society but it is a universal problem that has strong roots in the societies of the men dominating societies. Pakistani writers have always tried to expose such ideologies through their writings, which allegedly justify the dominance of men over women. Human relations are reciprocal and cannot be directed from one side. The approach employed is qualitative with techniques of textual and thematic analysis keeping claims in mind, presented by Lois Tyson in his book Critical Theory Today. It was found, through the analysis of the story, that women are oppressed economically, politically, socially, and psychologically and they are treated as the ‘other’: objectified and marginalized, by men not only in the east but all over the world. However, the element of ‘revolt against biased ideologies’ is also very significant through the rebellious behavior of the character and emphasizes bringing change in the societies.

Systematic Objectification of Eastern Women in Western Society A Case Study of ‘The Optimist’ by Bina Shah

Patriarchy is deeply rooted in all societies of the world and many sociological, political and religious reasons are behind this ideology. This was not highlighted by western writers in the past but it exists in the western modern world is also as presented by Loius Tyson (2006). He has claimed that women are kept oppressed economically, politically, socially and psychologically by patriarchy, moreover, women are considered as the ‘other’, therefore objectified and marginalized. The writings of eastern writers like the short story ‘The Optimist’ written by Bina Shah verifies the claims of Tyson. The narrated story proves that oppression of women is not the issue of eastern western society but it is a universal problem that has strong roots in the societies of the men dominating societies. Pakistani writers have always tried to expose such ideologies through their writings, which allegedly justify the dominance of men over women. Human relations are reciprocal and cannot be directed from one side. The approach employed is qualitative with techniques of textual and thematic analysis keeping claims in mind, presented by Lois Tyson in his book Critical Theory Today. It was found, through the analysis of the story, that women are oppressed economically, politically, socially, and psychologically and they are treated as the ‘other’: objectified and marginalized, by men not only in the east but all over the world. However, the element of ‘revolt against biased ideologies’ is also very significant through the rebellious behavior of the character and emphasizes bringing change in the societies.

Historical Psychology

A growing body of evidence suggests that many aspects of psychology have evolved culturally over historical time. A combination of approaches, including experimental data collected over the past 75 years, cross-cultural comparisons, and studies of immigrants, points to systematic changes in psychological domains as diverse as conformity, attention, emotion, morality, and olfaction. However, these approaches can go back in time only for a few decades and typically fail to provide continuous measures of cultural change, posing a challenge for testing deeper historical psychological processes. To tackle this challenge most directly, computational methods emerging from natural language processing can be adapted to extract psychological information from large-scale historical corpora. Here, we first review the benefits of psychology as a historical science and then present three useful classes of text-analytic techniques for historical psychological inquiry: dictionary-based methods, distributed-representational methods, and human-annotation-based methods. These represent an excellent suite of methodologies that can be used to examine the record of “dead minds.” Finally, we discuss the importance of going beyond English-centric text analysis in historical psychology to foster a more generalizable and inclusive science of human behavior. We propose that historical psychology should incorporate and further develop a variety of text-analytic approaches to reliably quantify the historical processes that gave rise to contemporary social, political, and psychological phenomena.

The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling

Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus–2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users’ perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level.

Understanding how the expression of online citizen petitions influences the government responses in China: An empirical study with automatic text analytics

Online participation is an important tool for citizens to express their demands, and it is essential for government agencies to respond to public petitions on time and effectively. However, few studies have focused on the rich textual characteristics within the expression of citizen petitions and the effects on the efficiency of government responses as well as response outcomes. In this study, we construct a theoretical model to explain how sentiments, fields, and detailed contents of online petitions influence the timeliness and text length of the corresponding government responses on the basis of discourse theory and government pressure theory. Using the data on the online petition platform of one provincial government in China, textual features are automatically extracted through text analysis techniques. Multiple regression models are employed with a heterogeneity analysis to examine the model. The results show that more negative sentiments, public interest, and detailed contents in the petitions will impede the timeliness of the corresponding responses and bring about longer responses since such petitions are usually complex and hard to address. These findings help better understand the hidden mechanisms of the interaction between governments and citizens, providing theoretical and practical implications.

Semantic Similarity Comparison Between Production Line Failures for Predictive Maintenance

With the introduction of Industry 4.0 into our lives and the creation of smart factories, predictive maintenance has become even more important. Predictive maintenance systems are often used in the manufacturing industry. On the other hand, text analysis and Natural Language Processing (NLP) techniques are gaining a lot of attention by both research and industry due to their ability to combine natural languages and industrial solutions. There is a great increase in the number of studies on NLP in the literature. Even though there are studies in the field of NLP in predictive maintenance systems, no studies were found on Turkish NLP for predictive maintenance. This study focuses on the similarity analysis of failure texts that can be used in the predictive maintenance system we developed for VESTEL, one of the leading consumer electronics manufacturers in Turkey. In the manufacturing industry, operators record descriptions of failure that occur on production lines as short texts. However, these descriptions are not often used in predictive maintenance work. In this study, semantic text similarities between fault definitions in the production line were compared using traditional word representations, modern word representations and Transformer models. Levenshtein, Jaccard, Pearson, and Cosine scales were used as similarity measures and the effectiveness of these measures were compared. Experimental data including failure texts were obtained from a consumer electronics manufacturer in Turkey. When the experimental results are examined, it is seen that the Jaccard similarity metric is not successful in grouping semantic similarities according to the other three similarity measures. In addition, Multilingual Universal Sentence Encoder (MUSE), Language-agnostic BERT Sentence Embedding (LAbSE), Bag of Words (BoW) and Term Frequency - Inverse Document Frequency (TF-IDF) outperform FastText and Language-Agnostic Sentence Representations (LASER) models in semantic discovery of error identification in embedding methods. Briefly to conclude, Pearson and Cosine are more effective at finding similar failure texts; MUSE, LAbSE, BoW and TF-IDF methods are more successful at representing the failure text.

New frontiers for qualitative textual data analysis: a multimethod statistical approach.

In recent years, the increase in textual data production has meant that researchers require faster text analysis techniques and software to reliably produce knowledge for the scientific-nursing community. Automatic text data analysis opens the frontiers to a new research area combining the depth of analysis typical of qualitative research and the stability of measurements required for quantitative studies. Thanks to the statistical-computational approach, it proposes to study more or less extensive written texts produced in natural language to reveal lexical and linguistic worlds and extract useful and meaningful information for researchers. This article aims to provide an overview of this methodology, which has been rarely used in the nursing community to date.