Semantic Analysis Techniques Research Articles

LEXICAL RELATIONS AND CONTEXTUAL MEANING ANALYSIS OF SURAH AL-KAHF IN ABDULLAH YUSUF ALI’S ENGLISH TRANSLATION

Direct translation of the Arabic Qur'an to other languages exists with the closest literal meaning due to the several absences of the exact equivalent meanings of words. Abdullah Yusuf Ali did one of the popular translation works from Arabic to English. One translation issue is the translator's bias, which leads this study to analyze the linguistic relations and contextual meanings in the English Qur'an translation to disclose the bias issue within and enclose prejudication arose. This qualitative study used the mixed technique of semantic analysis in four steps. The data of this research is the English translation of Chapter Al-Kahf (The Cave) verses 65-82 by Abdullah Yusuf Ali. The results of this study enclosed four types of lexical relations and four types of contextual meaning. Also, this study unintentionally found that the synonymy and antonymy pair categories consist of one verse pair and a different verse pair. Uniquely, a category of homonymy and five unrevealed patterns of contextual meaning used are also found in this study. This study concluded that the English translation of the Qur'an, Chapter Al-Kahf verses 65-82 by Abdullah Yusuf Ali has used several procedures to preserve the message within the Qur'an from being misinterpreted by the reader.

EmoConfident Interviewer :An AI Mock Interview Evaluator

Abstract: Interviews hold great significance for candidates as it's the moment when their hard work is put to the test in hopes of achieving their desired and successful life results. It plays a crucial role in our educational system and hiring process by helping to identify the best applicant based on the necessary abilities. We can do better by improving our communication and confidence skills through mock interviews. This article introduced new way to practice for interviews using AI-based mock interview platform. The three characteristics that our system will utilize to evaluate the user are emotions, confidence, and knowledge base. A deep learning CNN algorithm uses facial expressions to determine emotion classify the emotion into one of the seven categories, and the basis for evaluating confidence is voice recognition through the use Python modules for Pydub audio and natural language processing. A web scraping module will map keywords to internet resources by extracting them from incoming answers. Semantic analysis technique is utilized for knowledge assessment and keyword mapping. So, using this method will help the job candidate feel more confident and less stressed or anxious before the actual job interview

Дискурсивная специфика компьютерных игр

The present paper refers to the current problem of semiotic signs’ specifics in the context of gaming discourse based on texts from action first-person shooter game Ultrakill developed by Finnish creators. The problem is relevant due to the influence that computer games have on people’s mind – in particular on the minds of the younger generation. Moreover, the study will help to understand the linguistic mechanisms of the impact of video games on players. The study used methods of semantic analysis, semiotic analysis, observation and description techniques. As a result, the research revealed that developers use intertextual units to create patterns that are familiar to the player, but which at the same time have new characteristics. While 66% of all intertext is universally known, obscure allusions play an important role in creating associations for the recipient as well, which proves the importance of intertext in video game discourse. Well-known allusions play a role in creating semantic images in the game, while the lesser known intertext has easy to understand semes that could provoke the player to action in game. The research identified the following types of intertext: allusions to songs, music albums, other games, myths, the Bible and literature. The synthesis of intertext and author’s text in level names gives the player an idea of the upcoming challenges without informing of the level context, and in some cases even intriguing the player. The combination of borrowings and original semiotic units in the names of characters creates images that are familiar to the player, but at the same time, it creates properties that are specific to this particular game. Dialogues are mainly inherent in this genre, but the conversations also have deviations from this genre, which is also done in order to affect the player – the author gives the recipient a familiar behavior pattern in order to replace it afterewards, causing cognitive dissonance to the player. Conclusions are drawn about the discourse mechanisms in video games, how they affect the player and for what purpose the developer uses one or another linguistic technique.

Urban mobility in the future: text analysis of mobility plans

Sustainable Urban Mobility Plans (SUMPs) or similar documents from 17 European capitals (published between 2010 and 2021) and the European SUMP guidelines have been analysed to understand how cities shape mobility and their transport systems. Text analysis is applied to identify development tendencies in a time- and cost-effective manner, without relying on traditional deep semantic analysis techniques. In addition to traditional statistical indicators, we introduce Category Term Frequency (CTF) as a new measure in text analysis. CTF reveals the number and proportion of words belonging to the same content group, namely specific mobility-related categories. The results indicate that categories describing general aspects such as the future, general transport, environment, and society are more prominently represented compared to more forward-looking categories like automation, electromobility, and sharing. The aggregated CTF of categories describing these emerging aspects is highest in the mobility plans of Copenhagen, Helsinki, Luxembourg, and Vienna, which are considered forerunners in their implementation. In general, the analysis concludes that despite recent technological developments and new business models, the examined mobility plans barely mention terms that would imply radical changes by the 2030s. Strategic documents and, thus, urban mobility developments suggest only a slow transition towards the expected levels of sustainable and smart urban mobility. These findings may contribute to understanding and (re)considering urban and transport development strategies in Europe. Furthermore, this text analysis framework provides planners and other experts with a novel tool to identify the focal areas of mobility-related (or other) documents.

Online Information Reviews to Boost Tourism in the B&B Industry to Reveal the Truth and Nexus

Grasping the concerns of customers is paramount, serving as a foundation for both attracting and retaining a loyal customer base. While customer satisfaction has been extensively explored across diverse industries, there remains a dearth of insights into how distinct rural bed and breakfasts (RB&Bs) can effectively cater to the specific needs of their target audience. This research utilized latent semantic analysis and text regression techniques on online reviews, uncovering previously unrecognized factors contributing to RB&B customer satisfaction. Furthermore, the study demonstrates that certain factors wield distinct impacts on guest satisfaction within varying RB&B market segments. The implications of these findings extend to empowering RB&B owners with actionable insights to enhance the overall customer experience.

Mapping the Problem Statement and Study Purposes to the Quantitative Analysis: Insights From Mixed Methods Perspectives

This study introduces a novel approach to assess the quality of doctoral dissertations using a mixed methods design, specifically the convergent parallel model. Initially, the research focuses on qualitative data obtained from the context, research problem statement, and study purpose within a doctoral dissertation. As an example of quantitative data, the study examines self-confidence in creating a database based on years of experience using a questionnaire. The qualitative analysis employs semantic and thematic analysis techniques, revealing a clear alignment between the identified themes and the quantitative evidence. These findings underscore the effectiveness of the mixed methods approach in comprehensively evaluating the quality of doctoral dissertations.

Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts

Abstract Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the current pre-trained language model is also difficult for them to learn its implicit knowledge effectively, which brings troubles to machine semantic analysis. The study uses CRF training to obtain a semantic analysis model of Chinese literary texts that recognizes the semantic relationship between two words. SVM is used to train classifiers for confusing categories, and the two semantic relations in the output of the CRF model are further recognized to determine the final semantic relations between word pairs. Finally, the LCQMC dataset is used as the experimental data, and the semantic analysis technique based on CRF and SVM is employed to obtain the participle, lexical, and dependent syntactic annotations. According to the results, the model’s correct rates on the LAS for paraphrase recognition and dependency analysis of Chinese literary texts are 74.83% and 92.05%, respectively. The study enhances the efficiency of semantic analysis of relevant Chinese texts and is crucial for the study on the semantic analysis of terms.

Integrating Multimodal and Generative AI in Design Research: Enhancing Ethnographic Methods with Data-Driven Analysis

This research examines the application of Multimodal and Generative AI in analyzing interior design trends within the South Korean market. The objective is to enhance traditional ethnographic research methods with AI's quantitative capabilities, offering a more comprehensive understanding of design preferences. This methodology uses AI tools to systematically analyze and categorize design elements from a large dataset of interior design images sourced from a relevant online platform. This process involves extracting key visual trends and patterns using AI-driven semantic analysis and clustering techniques. The study identifies and classifies four dominant design trends, providing a structured overview of current market preferences. By integrating AI into design trend analysis, the study demonstrates a novel approach to understanding consumer preferences, potentially influencing future design decisions. This use of AI in design research broadens the scope of traditional ethnographic studies in design and provides practical insights for designers and industry practitioners, suggesting a shift towards more data-driven design practice.

What is Industry 4.0: a Review of the Literature Using the Conceptual Bibliographic Method

Purpose: Scientific production has tried to understand the various evolutionary intricacies of Industry 4.0, the so-called fourth industrial revolution. As discoveries increase and deepen, there must also be an effort to understand the frontiers of scientific knowledge, its most noticeable and studied aspects, and those attributes that have only now been perceived. It is, therefore, necessary to periodically take stock of what science knows about the phenomenon to understand where it is headed and which paths have already been taken. Therefore, the need to take inventory of the current state of knowledge about Industry 4.0 was the theoretical justification for this study.
 
 Theoretical framework: The theoretical architecture of this study consisted of the transformations that the industrial revolutionary stages caused in production systems, firstly, and then for the entire society. It involves aspects from various areas of scientific knowledge to understand the different perceptions contained in the scientific literature on the phenomenon of Industry 4.0.
 
 Design/Methodology/Approach: The conceptual bibliographic method was used to generate answers to three guiding questions: What are the main approaches to Industry 4.0? What are its main attributes? What is its irradiation logic? All conceptual definitions in scientific studies published in English and Portuguese available in the Google Scholar database from 2019 to 2020 were analyzed, with a total of 19 studies. The semantic analysis technique was used to identify the approaches and attributes, which were then organized by semantic proximity, constituting the semantic groupings on which the results were generated and interpreted.
 
 Findings: The results showed the existence of eight groups of approaches (procedures, evolution, time, grouping, transformation, layer, organization, and model) and twelve groups of attributes (industrialization, transformation technologies, information technologies, integrated system, processes, society, quality, knowledge, planning, market, value chain, and automation). These discoveries made it possible to understand that Industry 4.0 has a driving force: knowledge, which spreads similarly in waves until it reaches the entire society and is not limited only to the interior of factories and economic organizations.
 
 Research, Practical, and Social Implications: Two implications of these findings stand out. The first is the dynamic nature of implementing Industry 4.0 in organizations, whether economic or not, which is done more quickly if knowledge and technologies are available or more slowly when expertise and technologies are fragile or non-existent. The second is teleological since the purpose of Industry 4.0 and all industrial revolutions is to continually improve production processes, even though these benefits often generate unwanted externalities, such as environmental impacts. This means that the progress of knowledge and its developments cannot be stopped but can be directed towards other purposes.
 
 Originality/Value: The main contribution of this study to science is to point out the current stage of the evolution of Industry 4.0 about previous revolutions. Three other contributions are also significant: identifying the main approaches to the phenomenon, its most prominent characteristics, and its irradiation dynamics.

Analyzing Cooperative Learning Thematically: An Exploratory Case Study With a Composite Qualitative Model

This study explores how educators implement Cooperative Learning (CL) in their teaching methods through an exploratory case study using Saldana's model (2016) as the initial coding framework. Thematic coding, guided by Segev's semantic analysis techniques (2022), uncovered diverse perspectives among participants, highlighting various themes supporting CL models. Nevertheless, a consistent theme that emerged was the central concept of CL within the semantic analysis graph. The amalgamation of semantic analysis and thematic coding combined open and theoretical coding for a comprehensive understanding of the data. Furthermore, the incorporation of semantic discourse evidence strengthened the analysis, resulting in a theoretical representation of CL.

Analysis of the issues that emerged in the revision of the national social studies curriculum in South Korea: Text mining and semantic network analysis of the comments at the public hearing on YouTube

In South Korea, curriculum is revised, made public and implemented under a system known as a nation led curriculum. The South Korean national curriculum was completely revised 10 times between 1946 and 2015. At present, a complete revision is underway to replace the current 2015 national curriculum which is called the 2022 revised national curriculum. This study aims to analyze stakeholders’ responses to the YouTube public hearing on social studies curriculum according to the 2022 revised national curriculum in South Korea in order to understand the context and causes of the issues that emerged. Text mining, semantic network analysis and word cloud techniques were employed to identify issues. As a result, three issues were identified in the social studies curriculum: the balanced development of general elective subjects in high school; the separation of social sciences and geography and division of textbooks in middle school social studies and the separate listing of four subjects, specifically geography, social sciences, history and morals. The issues revealed in this study provide beneficial implications for future social studies curriculum development, revision as well as the development of future research.

Struktur dan Fungsi Bahasa Mantra dalam Masyarakat Jawa

Mantra is a distinctive choice of words, the meaning of which can only be known through in-depth cultural reading based on the culture in which the mantra lives. This study aims to describe the structure and function of the mantra language in Javanese society. The research method uses a qualitative descriptive approach. Sources of research data are Javanese mantras collected by purposive sampling. Data were analyzed by semantic analysis. The validity of the data used is semantic validity with semantic analysis techniques. The results of research on Javanese mantras are as follows: 1) the structure of the spell consists of a title, opening, suggestion, purpose, and conclusion of the spell; 2) the function of the mantra based on the user's purpose is generally divided into individual functions and social functions. The cultural aspects surrounding the spell have certain functions which are believed to have magical powers by the people. In Javanese culture, mantras have a special position in the religious system and public beliefs. The community also believes that each mantra recited has a different function according to the contents of the mantra recited by the speaker.
 Keywords: function; Javanese; mantra; structure

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.

XL-BPMN Model-Based Service Similarity Measurement Technique

In service-oriented developments, existing studies do not give lots of efforts on a formalized and systematic method for measuring similarities between services for their reuse in business models. This deteriorates the reusability of the constructed service due to the developers’ intuition and informal service analyses. In this paper, we propose a technique for measuring similarity of services by analyzing syntax and semantics between services in the eXtended Layered business process modeling notation (XL-BPMN) model. First of all, the profiles of the formalized attributes for specifying services are defined, and the criteria for determining service similarities are established. To measure similarity between services, a technique both a syntactic similarity analysis facilitated by the XL-BPMN model-based edge counting method and a semantic similarity analysis based on meta data registry (MDR)-applied service attributes is specified. To automate analysis, a tool that can support the semantic similarity analysis technique is implemented. An online shopping mall system is investigated and evaluated to verify the effectiveness of the proposed technique. The similarity measurement technique, which is further formalized at upper business levels, can improve the accuracy of service analyses and enhance service reusability by distinguishing services with high similarity levels as common services.

A latent semantic analysis method for ranking the results of human disease search engine

The human disease search engine based on the search query about disease factors (symptom, cause, position happening symptoms, i.e.,) helps users to conveniently diagnose the disease they may have anytime, anywhere. Therefore, the disease results returned by the search engine need to be accurate and ranked reasonably so that users can know which disease has the highest probability for their search query. We propose a method to arrange the returned diseases based on the latent semantic analysis (LSA) technique. This method helps to rank the disease results reasonably and meaningfully because it not only exploits the matching term frequency-inverse document frequency (TF-IDF) scores between the disease factors in the query and the disease results, but it also exploits the implicit relationship between the disease factors in the search query and the disease factors in the disease results.

Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques

Clickbait headlines are misleading headiness designed to attract attention and entice users to click on the link. Links can host malware, trojans and phishing attacks. Clickbaiting is one of the more subtle methods used by hackers and scammers. For these reasons, clickbait is a serious issue that must be addressed. This paper presents a method for identifying clickbait headlines using semantic analysis and machine learning techniques. The method involves analyzing thirty unique semantic features and exploring six different machine learning classification algorithms individually and in ensemble forms. Results show that the top models have an accuracy of 98% in classifying clickbait headlines. The proposed models can serve as a template for developing practical applications to detect clickbait headlines automatically.

Anthropolinguistics analysis on vocabulary of agricultural tools and activities in the Kuala dialect of Banjarese

This research is a preliminary study on vocabulary of agricultural tools and activities in the Kuala dialect of Banjarese using an anthropolinguistics approach. This study aims to describe and explain anthropolinguistics analysis of the vocabulary of agricultural tools and activities in the Kuala dialect of Banjarese. The anthropolinguistics research method from Danesi (2004) is utilized in this research. The data analysis technique uses semantic analysis techniques in anthropolinguistics. There are some findings in this study, they are: (1)Vocabulary of agricultural tools and the activities of sabalum batanam (prabatanam) in the Kuala dialect of Banjarese, (2) Vocabulary of agricultural tools and activities of wayah batanam (planting time) in the Kuala dialect of Banjarese, (3)Vocabulary of tools and farming activities of wayah mangatam (harvesting time) in the Kuala dialect of Banjarese, (4) Vocabulary of tools and activities of mangatam (post-harvest) in the Kuala dialect of Banjarese, and (5) Activity vocabulary related to the tradition of limbah tuntung maulah banih becomes baras (after processing paddy grains becoming rice) and mahadang musim batanam (waiting for planting) in the Kuala dialect of Banjarese. The pedagogical research implications are very useful for the development of teaching materials for agricultural vocabulary with local content in Banjarese.

Лингвокогнитивное моделирование категории "участник дискурса" (на материале интернет-мемов профессиональных сообществ юристов в социальных сетях)

The aim of the research is to identify semantic models of humorous utterances that express the stereotypical ideas of the lawyers’ professional community about the content of the categories of its participants in informal settings. The research methodology integrates two approaches: a critical analysis of discourse and a semantic approach to humour based on scripts by Salvatore Attardo and Victor Raskin. We applied the following analysis model: (1) discursive analysis of texts in accordance with the procedure presented in the papers of foreign and Russian scholars; (2) analysis of humour in accordance with the six-level model of analysis proposed by Attardo; (3) as the research includes transformed statements consisting of a complex of elements and their interrelationships to varying degrees, the methodology is supplemented by the theory of conceptual integration developed by Gilles Fauconnier and Mark Turner. The research methods are: discourse analysis, including standard techniques of communicative-pragmatic and semantic analysis; content analysis, the semantic categories of which are the parameters of the sociocommunicative level of the model, and the units are their typical lexical markers. The data include Internet memes constituting the content of professional legal groups on the social networks Instagram and Facebook. The data include 260 units. The analysis revealed a quantitative predominance in Internet memes expressing the subjects of legal discourse of models based on contrast and referential humour. The semantic dominants actualised in such models coincide with the stereotypical attitudes currently existing in society in relation to the legal community and its professional participants. The uneven distribution of models in accordance with the categories of participants indicates the predominance of reflection in relation to several professional participants – an advocate and a lawyer: intensity of professional activity and high fatigue, prestige of professional status and high social responsibility of professional participants, competence and professionalism (unprofessionalism), complexity and incomprehensibility of the language, high profit. In relation to non-professional participants (client/ accused/ defendant/ respondent/ witness), the stereotypes about their stupidity, greed, arrogance, venality are replicated. Referential models are distinguished by the diversity of the subjects represented and the uniformity of the meanings expressed: the subject’s originality of thinking, cunningness and resourcefulness. Semantic models based on conceptual integration were identified only in relation to professional participants – a lawyer. They actualise such values as professionalism, reliability, competence; stress associated with responsibility at work, danger and charm of the profession, special status; superiority; friendliness; help and superpowers of the professional.

A Novel Spam Classification System for E-Mail Using a Gradient Fuzzy Guideline-Based Spam Classifier (GFGSC)

Spam messages have increased dramatically in recent years even as the number of email clients has grown. Email has already become a valuable way of communicating because it saves time and effort. However, numerous emails contain unwelcome content known as spam as a result of social platforms and advertisements. Despite the fact that many techniques have already been created for spam mails categorization, none of them achieves 100 percent efficiency in analyzing spam messages. So, in this research, we propose a novel Gradient Fuzzy Guideline-based Spam Classifier (GFGSC) for classifying the spam e-mails as spam or non-spam. This research uses four types of datasets and these datasets are pre-processed using normalization. Then the set of data can be extracted using Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) techniques. The aspects are selected using Information Gain (IG) and Chi-Square (ChS) techniques. And the GFGSC classifier can be used for classifying the data as spam or non-spam with better effectiveness. Finally, the performances are examined and these metrics are matched with the existing approaches. The results are obtained using the MATLAB tool.

Analysis of the challenges of a private cardboard recycling company to fulfill its mission in the city of Manaus, Amazonas

The trade in materials of cellulosic origin, such as paper, cardboard and others, has shown significant growth in many countries, mainly because they are used as packaging coatings and other forms of reuse, fundamentals of reverse logistics. In this sense, this work aimed to analyze the challenges that a private cardboard recycling company faces to fulfill its organizational mission in the city of Manaus, capital of the state of Amazonas. For this, it followed the steps foreseen in the scientific-technological method. Data were collected through a script of interviews and an observation protocol, organized and analyzed with the help of semantic analysis techniques, identifying central terms of the responses collected by interviews and compared with the observed data; the results were generated with triangulation techniques of the answers obtained with the two instruments and interpreted in a comparative way with the theoretical framework of reference. The results showed that a) the company contributes to environmental preservation through the correct destination of materials and cleaning of public places, b) these contributions are made through obedience to legal procedures, c) its challenges are to have access to all customers industrial and residential areas and make the population aware of the importance of environmental preservation; d) population and governments are the agents that can help overcome these challenges through e) awareness of the importance of sustainability (population) empowering and making the population put sustainability into practice (government). The conclusion shows that, albeit slowly, there is a tendency to increase this desired awareness.