Textual Elements Research Articles

The Comparison Study of the Language Composition of Daily Newspapers: The Case of Mongolia and Inner Mongolia

Our study aims to compared language in daily newspapers from Mongolia and Inner Mongolia, analyzing their content for similarities and differences. We focused on articles, headlines, and other textual elements to understand language use at various levels. Daily newspapers offer insights into real-life language usage, reflecting current events and social trends. By comparing these newspapers, we aim to uncover linguistic variations influenced by political, social, and cultural contexts, contributing to our understanding of language dynamics in the Mongolian-speaking world.

Enhancing Social Media Insights: Leveraging Artificial Intelligence for Sentiment Analysis

Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

Seeing and Thinking about Urban Blue–Green Space: Monitoring Public Landscape Preferences Using Bimodal Data

Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively understand public preferences. This paper proposes a method to evaluate public landscape preference from the perspective of seeing and thinking, takes the examples of seven parks around the Dianchi Lake in Kunming, China, and analyses the social media data by using natural language processing technology and image semantic segmentation technology. The conclusions are as follows: (1) The public exhibits significantly high positive sentiments towards various UBGSs, with over 93% of comments expressed positive sentiments. (2) Differences exist in the frequency and perception of landscape features between image and text modalities. Landscape elements related to stability are perceived more in images than in text, while dynamic and experiential elements are perceived more in text than in images. (3) In both modalities, the distinctive landscape features of parks are more frequently perceived and preferred by the public. In the end, the intrinsic links between landscape elements and public sentiment and preferences are discussed, and suggestions for design and management improvements are made to consolidate their health benefits to the public.

The Séance of Reading: Uncanny Designs in Modernist Writing, Thomas J. Cousineau

‟The Séance of Reading: Uncanny Designs in Modernist Writing” (2023), a book arising out of Thomas J. Cousineau’s long and fruitful teaching experience and his extensive engagements in Romanian academia as a Fulbright scholar, entices readers to appreciate key canonical works spanning American and European literature through the lens of the Romanian ballad ‟The Legend of Master Manole” and Mircea Eliade’s view of mythical creation by sacrifice. The aesthetic myth of master-builder Manole lends magnitude and resonance to nine modernist texts set in harmonious juxtaposition for the purpose of revealing and restoring the sacrificed textual elements within. Most valuably, the author makes the reader register the intricate relationship between the sacrificed protagonist and the architectural body of the literary monuments discussed throughout his book, which successfully brings together different literary genres, capitalizing on the scenario of death and ontological rebirth.

SIDU-TXT: An XAI algorithm for NLP with a holistic assessment approach

Explainable AI (XAI) is pivotal for understanding complex ’black-box’ models, particularly in text analysis, where transparency is essential yet challenging. This paper introduces SIDU-TXT, an adaptation of the ’Similarity Difference and Uniqueness’ (SIDU) method, originally applied in image classification, to textual data. SIDU-TXT generates word-level heatmaps using feature activation maps, highlighting contextually important textual elements for model predictions. Given the absence of a unified standard for assessing XAI methods, to evaluate SIDU-TXT, we implement a comprehensive three-tiered evaluation framework – Functionally-Grounded, Human-Grounded, and Application-Grounded – across varied experimental setups. Our findings show SIDU-TXT’s effectiveness in sentiment analysis, outperforming benchmarks like Grad-CAM and LIME in both Functionally and Human-Grounded assessments. In a legal domain application involving complex asylum decision-making, SIDU-TXT displays competitive but not conclusive results, underscoring the nuanced expectations of domain experts. This work advances the field by offering a methodical holistic approach to XAI evaluation in NLP, urging further research to bridge the existing gap in expert expectations and refine interpretability methods for intricate applications. The study underscores the critical role of extensive evaluations in fostering AI technologies that are not only technically faithful to the model but also comprehensible and trustworthy for end-users.

INTERNATIONALIZATION OF SOFTWARE AND ITS LOCALIZATION INTO UKRAINIAN

The article explores the effects of the internationalization process on adapting software for the Ukrainian locale, emphasizing the linguistic, communicative, and socio-cultural challenges encountered by localizers and translators. It also discusses the strategies employed to address these challenges, considering the unique characteristics of the product and the production processes involved in adapting software for various markets. The study relies on the localized versions of software from Microsoft and Google, along with an examination of their style guides. The goal of the research is to assess the impact of internationalization on the adaptation of software for the Ukrainian audience by delineating strategies and identifying key localization issues. Comparative and contextual analysis methods are used to examine the original and localized versions of the software. The findings from the analysis of supporting documentation and Ukrainian localized products demonstrate that localization is a multifaceted process designed to adapt a product based on the interaction between differenlocales. The interaction properties humans and software to meet the specific requirements and characteristics of influence the architecture and textual elements of the software, as well as the sequence of operations necessary for adaptation to specific locales. Throughout the internationalization of software and the preparation of materials for further localization, certain elements are identified as vulnerable due to linguistic, communicative, and socio-cultural factors, which can present challenges during adaptation to local needs. The linguistic issues associated with Ukrainian localization stem from the specific alphabet system and grammatical features of the Ukrainian language. Communicative aspects determine the level of formality in the software's interaction with Ukrainian users. The socio-cultural characteristics of the Ukrainian locale are the most dynamic and, therefore, may not be fully encapsulated in localization guidelines. The analysis of the auxiliary documents provided to localizers illustrates the developers' commitment to delivering high-quality localized software to Ukrainian users. This commitment is achieved by identifying and addressing the linguistic, communicative, and socio-cultural elements that play a crucial role in successful localization.

四柱五行 중 無存五行 사주에 대한 고찰

The purpose of this study is to study Myeongrihak focusing on effects of problems of Five elements no existence(五行無存) of Trees (木) of the five elements. This is an examination of the four pillars of Myeongjo as analyzed in actual cases person who appears in the book of 『Jeokcheonsucheonmi(適 天髓闡微)』In the Four Pillars Myeongjo, imbalance and disharmony occur due to the abnormality and non-existence of any of the five functions. And, in more detail, we examine case studies in ancient books to see what kind of life a person with a fortune in which one of the five elements was not fully studied lived. In the Four Pillars Myeongjo, imbalance and disharmony occur due to the absence of any of the five elements and the absence of any one of the Five Elements. And, among the Five Elements of the Tribe, in more detail, we examine the case studies in the ancient scriptures to see what kind of life a person with a fortune in which one of the Five Elements is not fully maintained lived. do. Accordingly, by looking for and examining a case of a certain Five Elements in the original text of Iljeongmu's 『Jeokcheonsucheonmi適天髓闡微』, I looked into the details of the life of Izumi and what kind of life the main character of the case actually lived. I studied the writings left by Lim Cheol- cho(任鐵樵)

MISTRA: Misogyny Detection through Text–Image Fusion and Representation Analysis

Detecting misogynous memes poses a significant challenge due to the presence of multiple modalities (image + text). The inherent complexity arises from the lack of direct correspondence between the textual and visual elements, where an image and overlaid text often convey disparate meanings. Additionally, memes conveying messages of hatred or taunting, particularly targeted towards women, present additional comprehension difficulties. This article introduces the MISTRA framework, which leverages variational autoencoders for dimensionality reduction of the large-sized image features before fusing multimodal features. The framework also harnesses the capabilities of large language models through transfer learning to develop fusion embeddings by extracting and concatenating features from different modalities (image, text, and image-generated caption text) for the misogynous classification task. The components of the framework include state-of-the-art models such as the Vision Transformer model (ViT), textual model (DistilBERT), CLIP (Contrastive Language–Image Pre-training), and BLIP (Bootstrapping Language–Image Pre-training for Unified Vision-Language Understanding and Generation) models. Our experiments are conducted on the SemEval-2022 Task 5 MAMI dataset. To establish a baseline model, we perform separate experiments using the Naive Bayes machine learning classifier on meme texts and ViT on meme images. We evaluate the performance on six different bootstrap samples and report evaluation metrics such as precision, recall, and Macro-F1 score for each bootstrap sample. Additionally, we compute the confidence interval on our evaluation scores and conduct paired t-tests to understand whether our best-performing model has significant differences from the other experiments or not. The experimental results demonstrate that the dimensionality reduction approach on multimodal features with a multilayer perceptron classifier achieved the highest performance with a Macro–F1 score of 71.5 percent, outperforming the baseline approaches in individual modalities.

Shaping The Way to Teach? Exploring the Adoption of Digital Multimodal Texts As Instructional Tools in EFL Teaching Practicum

This research scrutinizes a case study conducted with 15 pre-service English as a Foreign Language (EFL) teachers in a microteaching class at the English Education Department at UIN Walisongo Semarang. The purpose of the study was to examine the forms, implementation, and reflection of digital multimodal instructional media utilized by pre-service teachers. Observations of classrooms and document analysis were employed to acquire data. Pre-service EFL teachers employ digital multimodal instructional media, including PowerPoint presentations, films, and songs, to enhance English language learning. These tools integrate text, images, video, and audio elements to teach language structures, vocabulary, and emotions effectively. Student portfolios showcase diverse educational strategies across different grade levels, using videos, songs, and PowerPoint presentations to engage students and reinforce language skills. Educator reflections underline the significance of digital multimodal media in simplifying complex topics, improving comprehension, and fostering a holistic learning experience. They also stress the importance of adequate equipment and resources for successful multimedia implementation, affirming the value of multimodal approaches in providing impactful and engaging English language education. The study highlights the potential for these digital tools to provide an engaging and interactive learning environment for EFL students. It is recommended that more research investigate ways for optimizing the use of digital multimodal media in diverse EFL teaching scenarios.

Multi-modal Metaphorical Analysis of Children’s Picturebook <i>Little Stone Lion</i>

Little Stone Lion stands out as a member of Xiong Liang's "Chinese Illustrated Book" series. This work tells the story from the perspective of the stone lion at the bridgehead of the town, recounting its observations, experiences, and feelings. This book integrates traditional Chinese ink painting techniques with cultural elements, presenting a Chinese-style visual creation. Drawing upon a qualitative research approach, the study employed a comprehensive analysis of Little Stone lion, focusing on identifying and interpreting the verbal, pictorial, and multimodal metaphors employed in the illustrations and their relation to the accompanying text. The study delved into the interplay between visual and textual elements, examining how they contribute to the creation of metaphoric meaning and literary depth in the picturebook. This research sought to uncover metaphorical meanings derived from the situational and conceptual-cognitive contexts as well as the underlying thematic and emotional layers embedded within the picturebook. As the only guardian deity of the town, the little stone lion has witnessed the growth and departures of countless generations, silently guarding this land and its people. Multimodal metaphors identified from camera angles, colors, and its plain monologue are enough to touch the heartstrings and evoke a deep sense of nostalgia for one's hometown and loved ones.

RecSOI: recommending research directions using statements of ignorance

The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that draws from statements of ignorance (SOIs) found in the research literature. By building researchers’ profiles based on textual elements, RecSOI generates personalized recommendations of potential research directions tailored to their interests. In addition, RecSOI provides context for the recommended SOIs, so that users can quickly evaluate how relevant the research direction is for them. In this paper, we provide an overview of RecSOI’s functioning, implementation, and evaluation, demonstrating its effectiveness in guiding researchers through the vast landscape of potential research directions.

The effect of tone, signature, and visual elements in compliance management systems disclosures on financial analysts’ decisions

Numerous corporate scandals, in conjunction with managerial misbehavior, demonstrate both the need for effective compliance management systems (CMS) and the relevance of compliance reporting. Disclosure elements are a major differentiator when reporting on CMS. This study investigates whether the tone, top managers’ signatures, and visual elements affect financial analysts’ perceptions and decisions. We use several theories from cognitive psychology which suggest that positive tone, the presence of signatures, and the combination of textual and visual elements can induce more favorable perceptions of compliance disclosure. In a 2 × 2 × 2 between-subjects experiment with 148 financial analysts from Germany, we manipulate tone (positive vs. negative), top managers’ signatures (present vs. absent), and visual elements (present vs. absent) to measure analyst perceptions of reliability, understandability, and usability, as well as credit risk, purchase, and recommendation decisions. We then sum up these dependent variables to create two new variables, perceptions, and decisions. Our results suggest that negatively toned compliance disclosure positively affects financial analyst perceptions, unless it is combined with a visual, as well as their decisions, unless it is combined with both a signature and a visual element. Additionally, signatures and a visual element have an overall positive effect on analyst perceptions. The study’s results confirm the relevance of disclosure elements in compliance reporting, which should be of interest to preparers, users, auditors, and regulators of compliance disclosure.

The Effectiveness of ChatGPT in Translating Chunky Construction Texts in Chinese Political Discourse

From the theoretical perspective of Chinese-English temporal-spatial cognitive differences, this study employs a self-constructed bilingual parallel corpus of political discourse totaling one million words. It extracts chunky construction texts with latent agents in the source language (Chinese) and corresponding texts of target language translations (English) for a comparative experiment between ChatGPT translations and those of human translators. Driven by the “Human-AI Interaction Model”, three different prompts were set up to conduct three rounds of translation testing with ChatGPT. The study discovers: 1) ChatGPT has limitations in actively understanding the implicit elements of the source Chinese texts that are driven by a strong spatial preference, which are also reflected in the target language English translations; 2) The “Human-AI Interaction Model” can guide and train ChatGPT effectively through continuous optimization of prompts, enhancing AI abilities in understanding the source language and achieving effective target language translation; 3) Currently, ChatGPT cannot replace human translators in translating chunky discourses of Chinese, but the collaborative interaction between human translators and AI is highly effective. By optimizing the design of prompt instructions, this research offers cognitive reference and operational paradigms to improve ChatGPT’s recognition and cognitive reconstruction capabilities of Chinese-English temporal-spatial differences, providing insights for future research, translation practice, and teaching in related areas.

(Re)framing online news through paratexts: a multisemiotic analysis of The New York Times in Chinese

ABSTRACT This study employs a multisemiotic approach to analyse various paratextual framing devices in online news texts, including news headlines, photographs and captions, as well as section names. Translated news articles about the 2014 Hong Kong protests were collected from the Chinese-language website of The New York Times (NYT) published between 28 September and 16 December 2014 and their original versions from its English website. The study finds that the Chinese edition mainly frames the events covered in the articles through paratextual rather than textual elements and, through those paratextual elements, a slightly different social reality to their English version for the events was (re)framed. This study underscores the influential role of paratextual devices in news translation, demonstrating how they can subtly shift the framing of events and potentially influence public perception.

That is not a pipe, Mon. Magritte. But, is this not a map? Some questions about map correspondence inspired through Barbara Buttenfield

ABSTRACT This essay considers a painting from Rene Magritte, The Treachery of Images, also known as “This is not a pipe,” the translation of the prominent statement Ceci n’est pas une pipe in the painting, which Barbara Buttenfield had prominently placed in her University of Buffalo office. It is an iconic and surrealistic twentieth century artwork (with a second version), widely copied and readily available online. Its visual juxtaposition of a textual element and detailed realist painting of a classical smoker’s pipe can quickly become more than a surrealistic play with words and images and poignantly stimulates associations and disconnections between image and object, text and object, and text and image. Viewers can also see a relevance of this painting to a number of representational issues involving images and maps. However, I want to focus in this essay on just two questions: 1) why do people affirm the connections between graphical map images and graphics in light of a fundamental disconnection between map and the represented territory or objects and 2) how do people still find sense in maps after we acknowledge the limits of any sense of correspondence between map image and experienced geography?

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

Assessing voice vs. writing quality

Abstract Using a mixed-methods design, the study aimed to distinguish voice features from the criteria considered in most L2 writing rating scales (e. g., topic development, organization, and quality of language use). Accordingly, a sample of 200 argumentative essays written in English was scored for voice, writing quality, and the critical features of academic writing such as text length, lexical sophistication, and grammatical complexity (see Cumming et al., 2005). Multiple regression and structural equation modeling (SEM) were conducted to verify which of and how the critical elements of academic writing interacted to determine the scores for voice and writing quality. Think-aloud protocols (TAPs) of raters scoring voice and writing quality were also compared. Voice was found an indication of genre competence, rated independently of language use and mostly based on the structure of arguments and the quality with which writers employed certain textual voice elements. In assessing writing quality, however, textual voice elements were dismissed, and the writing quality scores mainly reflected the quality of language use rather than the argument structure. The study provides implications for second language writing assessment and instruction, as well as for future research to be conducted.

Despair, Hope, And Politics: A Semiotic Analysis Of Online Media Cartoons On The Turkey And Syria Earthquake 2023

The Turkey and Syria earthquake of 2023 caused widespread devastation and loss of life, resulting in widespread despair and hopelessness among the people. In the aftermath of the disaster, social media was flooded with caricatures that sought to represent the political and social realities of the affected regions. This research paper presents a semiotic analysis of online media cartoons using the Barthesian modal to understand the representation of despair, hope, and politics in the context of the Turkey and Syria earthquake of 2023. Through a qualitative analysis of purposively sampled cartoons, this study explores how dominant emotions and political messages are conveyed through visual and textual elements and how they shape public perception of the event. The findings suggest that the cartoons use a range of semiotic modalities, such as images, text, and symbolic representations, to convey political messages about the earthquake and its aftermath. The paper contributes to the growing research on semiotics and visual communication. It highlights the potential of online cartoons as a rich source of social and political insights in the context of the earthquake and how people respond to it.

Building Suspense: A Critical Examination of Foreshadowing Devices in TV Series Opening Credits

This paper explores the intricate use of foreshadowing devices within the opening credits of television series, unraveling the artful integration of visual, musical, and textual elements. Through detailed case studies of iconic shows such as Game of Thrones, Stranger Things, and Westworld, we dissect the deliberate choices made by creators to engage audiences from the very beginning. The analysis uncovers the narrative nuances embedded in symbols, musical scores, and textual clues, highlighting their role in setting the stage for complex and immersive storylines. Ethical considerations, methodological challenges, and the limitations of the study are also addressed. By contributing insights to television studies and narrative theory, this paper emphasizes the significance of opening credits as a canvas for storytelling in the evolving landscape of contemporary television.

Features of the implementation of speech strategies and tactics in the slogan of television food advertising

The article is devoted to the study of speech strategies and tactics in the slogan of television food advertising. The slogan as an element of the advertising text plays a significant role in influencing the consciousness and behavior of the addressee. Slogans implement various speech strategies and tactics to ensure the success of advertising communication. The difficulty of studying this phenomenon lies in a short formulation with great strategic potential and its uncertain relationship with the main text of television advertising. The relevance of this study is related to the importance of the slogan in the advertising text and the prospects for studying speech strategies and tactics in the slogan of television food advertising. The article aims to identify the features of speech strategies and tactics in the slogan of television food advertising. The method of linguistic and pragmatic analysis was used in the work, which makes it possible to identify speech strategies and tactics in the slogan of television advertising of food products and describe the linguistic features of their implementation. The novelty of the research lies in a comprehensive examination of speech strategies and tactics in the slogan, taking into account the main advertising text. As a result of the analysis, corporate slogan, product slogan and festive slogan were highlighted in television advertising. The slogans of television food advertising highlight presentation strategy, convincing strategy, persuading strategy and reminiscent strategy, as well as tactics and techniques within the framework of relevant strategies. The linguistic means of verbalization of techniques used within the framework of appropriate speech strategies and tactics in television advertising of food are described. The correlation of their implementation with the main advertising text has been established. The slogan of a TV advertisement usually implements several strategies and tactics at the same time. The implementation of the convincing strategy in a product slogan is relevant to the main advertising text. The implementation of other strategies in the slogan is not related to the main advertising text.