Textual Content Research Articles

A short video sentiment analysis model based on multimodal feature fusion

With the development of the internet, the number of short video platform users has increased quickly. People's social entertainment mode has gradually changed from text to short video, generating many multimodal data. Therefore, traditional single-modal sentiment analysis can no longer fully adapt to multimodal data. To address this issue, this study proposes a short video sentiment analysis model based on multimodal feature fusion. This model analyzes the text, speech, and visual content in the video. Meanwhile, the information of the three modalities is integrated through a multi-head attention mechanism to analyze and classify emotions. The experimental results showed that when the training set size was 500, the recognition accuracy of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.96. The F1 score was 98, and the average absolute error value was 0.21. When the validation set size was 400, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 2.1 s. When the iterations were 60, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.9 s. The experimental results show that the proposed multimodal sentiment analysis model based on modal contribution recognition and multi-task learning has good model performance and can accurately identify emotions in short videos.

Implementasi Model Pembelajaran Make a Match pada Siswa Kelas XI-B1 SMAN 9 Surabaya untuk Meningkatkan Hasil Belajar Teks Cerpen

This study uses the Make a Match learning methodology in class XI-B1 SMAN 9 Surabaya to increase student learning outcomes on short story text content. 36 students participated in two cycles of the study. Following the implementation of the Make a Match learning paradigm, the results demonstrated a significant increase in student participation and learning outcomes. Despite some students still falling short of the KKM, the number of students in cycle 1 who received good and very good categories grew. Every student outperformed the KKM in cycle 2, with 41.7% placing them in the outstanding group and 58.3% in the good category. This approach worked well to increase overall student participation and learning results.

The International Red Cross in contemporary Polish history textbooks: An overview

The main aim of the article is to present how the issues pertaining to the creation and activities of the International Red Cross and the figure of its originator and founder, Henry Dunant, are presented in Polish history textbooks. History lessons at school are a suitable platform to introduce issues related to the concept of humanitarianism. Lessons that mention the establishment and operations of the International Red Cross and its national branches make a valuable contribution to raising awareness of humanitarianism and human rights. It is worth looking at how these issues are treated in school textbooks, which are one of the main sources of students' knowledge of history. Primary and secondary school textbooks currently available on the publishing market and used by active teachers were analysed. Textual content and iconography were analysed, as well as the tasks proposed for classroom use. In order to answer the research questions, a critical textual analysis of the content of twenty-one textbooks was carried out. Textbooks published between 2017 and 2023, which are on the official list of textbooks approved for use in schools, were the subject of analysis. The article also reveals the presence of the Polish Red Cross on the pages of these textbooks.

Enhancing Museum Experiences: A Multi-Institution Mobile Multimedia Delivery System Using BLE Beacons

This paper presents a solution using Bluetooth Low Energy (BLE) beacon technology to enhance visitor experiences in museums, both indoors and outdoors. Organizations using the developed mobile service can associate text, images, video, and audio content to specific exhibits, providing a dynamic and engaging encounter for visitors. This innovative approach extends applicability to a wide range of organizations, not just museum environments.

A Survey of Text Watermarking in the Era of Large Language Models

Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent advancements in large language models (LLMs) have revolutionized these techniques. LLMs not only enhance text watermarking algorithms with their advanced abilities but also create a need for employing these algorithms to protect their own copyrights or prevent potential misuse. This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking. This survey aims to provide researchers with a thorough understanding of text watermarking technology in the era of LLM, thereby promoting its further advancement.

Herding and investor sentiment after the cryptocurrency crash: evidence from Twitter and natural language processing

Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.

English Preservice Teachers’ Perceptions of Listening Comprehension Difficulties

This study examines the perceptions of listening comprehension difficulties among English preservice teachers at a university in Thailand. Survey-based research was conducted using a questionnaire designed by Ahkam Hasan Assaf. The questionnaire consisted of 45 close-ended questions on a five-point Likert scale. Out of 98 English preservice teachers selected through simple random sampling, 63 respondents completed an online questionnaire. Data were analyzed using descriptive statistics. The results indicated that the greatest difficulties were associated with the physical setting (M=3.84) as the most challenging, followed by content of spoken text (M=3.77), the speaker (M=3.72), failure to concentrate (M=3.51), difficulties related to the listener (M=3.49), and linguistic features (M=3.45). Rated as moderate, the importance of listening skill (M=3.05) was rated the least. The study reveals how individual learner differences impact listening comprehension and trigger the need for effective educational approaches, such as diagnostic assessments to identify specific listening challenges faced by students, allowing for customized listening exercises. Additionally, curriculum designers can incorporate diverse and comprehensive listening components to directly target these difficulties. This also emphasizes the need for enhanced teacher training programs that focus on pedagogical strategies, including adaptive teaching methods and effective use of technology, to improve the engagement and effectiveness of listening lessons.

Text2Face: Text-Based Face Generation With Geometry and Appearance Control.

Recent years have witnessed the emergence of various techniques proposed for text-based human face generation and manipulation. Such methods, targeting bridging the semantic gap between text and visual contents, provide users with a deft hand to turn ideas into visuals via text interface and enable more diversified multimedia applications. However, due to the flexibility of linguistic expressiveness, the mapping from sentences to desired facial images is clearly many-to-many, causing ambiguities during text-to-face generation. To alleviate these ambiguities, we introduce a local-to-global framework with two graph neural networks (one for geometry and the other for appearance) embedded to model the inter-dependency among facial parts. This is based upon our key observation that the geometry and appearance attributes among different facial components are not mutually independent, i.e., the combinations of part-level facial features are not arbitrary and thus do not conform to a uniform distribution. By learning from the dataset distribution and enabling recommendations given partial descriptions of human faces, these networks are highly suitable for our text-to-face task. Our method is capable of generating high-quality attribute-conditioned facial images from text. Extensive experiments have confirmed the superiority and usability of our method over the prior art.

A Detecting Fake News Using Machine Learning: A Comparative Study of Techniques

Many people get their news via the Internet and social media platforms, and given the rapid growth of these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm society and the individual is known as fake news. By deliberately spreading false information, the media distort public opinion and threaten the social order by leading people to believe things that are not true. With the massive expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it poses some difficulties due to limited resources (such as datasets and published research). This paper presents diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy and can save time and effort by automatically identifying fake news, especially in light of the massive amount from materials available on the Internet.

Methodology of Using Large Language Models to Solve Tasks of State and Municipal Government for Intelligent Abstracting and Automatic Generation of Text Content

Large language models (LLM) are finding new areas of application in practice, including the sphere of public and municipal administration. To increase the efficiency of the practical application of large language models rules and methods of interaction with them are developed, taking into account the specifics, a wide range of their possible use and increasing accessibility. The article examines the issues of improving the efficiency of large language models with various types of content using prompt engineering techniques. An analysis of a significant number of prompts for large language models and methods for their formation is presented. The article discusses the possibilities of using large language models, trained (customizable) using creative prompting for intelligent abstracting of various content with the subsequent generation of original texts and text documents for the sphere of state and municipal administration. The proposed methodology makes it possible to effectively integrate knowledge from various sources into LLM training and turn it into a truly intelligent tool that expands the possibilities of its work. When applying this approach, the LLM acts as a powerful intelligent assistant that allows you to generate a document authored by the user of the system. The use of large language models opens up wide opportunities for employees in the field of state and municipal administration to automate the process of creating thematic texts, text reports, qualification papers, reviews and analytical notes. It also allows users to see possible new meanings, previously unnoticed associations, and even generate new ideas in the field of management in the process of analyzing the texts received during the abstract. The authors have shown that in order to improve the quality of intellectual abstracting, it is necessary to carry out the iterative use of different methods of teaching (tuning) LLM. At the same time, the initial selection of texts for training, which is made by the user based on his/her own knowledge of the subject area, is important.

A pragmatic comprehension of scientific and classical aspects of Rachana Sharir—A systematic review

Ayurveda, a flourishing traditional medical system renowned for its extensive knowledge, includes Rachana Sharir as a fundamental subject. Rachana Sharir Vigyan explores the anatomical aspects (Rachna) of the human body. The immense knowledge of the ancient wisdom of Ayurveda is not comprehensively described yet. The Samhitas and Vedas of Indian literature by different acharyas elaborate on how the human body evolved from a combination of Panchamahabhutas and Atma (Chetana). This review aims to elucidate the classical aspects of Rachana Sharir from Ayurvedic texts and establish correlations with contemporary scientific knowledge. Classical texts such as the Samhitas authored by various acharyas were systematically reviewed to gather insights into the origin, anatomical positions, numerical compositions, and classifications of human body parts, organs, tissues, and systems. The review reveals detailed principles and concepts from Ayurvedic literature, emphasizing contributions from Acharya Sushruta in the Sharir Sthana, particularly his meticulous descriptions of cadaveric studies and preservation techniques. Classical books explained the origins of human bodily parts, their anatomical placements, organs, tissues, systems, and more. These fall under human anatomy (Rachana Sharir), and this article explains its classical aspect with scientific and modern science linkages. In Sharir Sthana, one of Ayurveda’s main principles, cadaveric research and preservation, is beautifully illustrated by Acharya Sushruta. Ayurveda has scientific principles, logic, and subtle knowledge that we must explore to apply its gifts to humanity. This essay shows that Samhitas contains pragmatic and scientific knowledge. Ayurveda combines science and philosophy. Ayurvedic knowledge’s pragmatic and scientific usefulness beyond textual content must be explored and validated to maximize its benefits for humanity.

Media representation of African individuals in Australia during the COVID-19 pandemic and its impact on mental health

Purpose The purpose of this paper is to explore the impact of media representation on the mental health of Australians of African descent during the COVID-19 pandemic. By analysing the media coverage of COVID-19 restriction breaches, particularly focusing on individuals from African backgrounds, the study aims to shed light on how racially charged narratives can contribute to emotional distress and exacerbate feelings of alienation within these communities. The findings highlight the detrimental effects of such portrayals, emphasising the need for more responsible and inclusive media reporting to safeguard the mental well-being of culturally and linguistically diverse populations. Design/methodology/approach The study employed media content analysis to explore representations of Australians of African origin versus the broader Anglo–Australian population during the COVID-19 pandemic, focusing on racial identity’s impact on news coverage of COVID-19 restriction breaches. Researchers classified and distilled extensive textual content, using a diverse sample from various ethnic-racial backgrounds, with an emphasis on African Australians within the CALD community. Data analysis was conducted using NVivo (version 12) software, following an inductive approach. Findings The findings underscore the consistent portrayal of people from African communities as outsiders and the racial profiling they experience in media coverage of significant issues like COVID-19. Originality/value There is very limited research that examines the impact of media coverage on African migrants during the COVID-19 pandemic.

Decoding Success: Investigating the Impact of Trait and State Strategies on Reading Test Performance among Thai High School Learners

The field of second language (L2) acquisition has increasingly emphasized learner strategies, cognitive and metacognitive methods, as crucial factors in individual differences and reading comprehension. Grounded in Bachman and Palmer’s communicative language ability (CLA) model, this study investigates how strategic competence in cognitive and metacognitive strategies impacts reading comprehension among Thai high school students. By employing quantitative methods, including structural equation modeling (SEM), the study explores how trait strategies (perceived strategic knowledge) and state strategies (actual strategy use) influence reading test performance. The research involved 685 students from a public high school who completed Likert scale questionnaires about their strategy use before and after comprehension tests. Results reveal that state strategies are employed more frequently than trait strategies and show a significant positive correlation with reading performance. While cognitive strategies like comprehension and memory are critical for understanding text content, metacognitive strategies like planning and monitoring improve learners’ ability to regulate their strategic application. However, evaluation strategies were found to be less frequently used. The study’s findings advocate for balanced training in both cognitive and metacognitive strategies to bridge the gap between strategic knowledge and effective application, thereby empowering learners to become more autonomous and proficient readers. Further research should investigate strategic competence in different cultural contexts, employ longitudinal studies, and expand the research to other language skills to gain a comprehensive understanding of L2 learners’ strategies.

ALUNOS COM BAIXA VISÃO E PROFESSORES DURANTE O ENSINO REMOTO EMERGENCIAL

In the context of the Covid-19 pandemic, measures were created to combat the virus, such as social distancing, which caused schools and other educational institutions to suspend their face- to-face activities, adhering to Emergency Remote Teaching (ERT). Adaptations and the use of different teaching strategies in this context were necessary and it was considered relevant to understand how regular class and Special Education teachers and students with Visual Impairment (VI) dealt with the particularities of the ERT. The objective of the present study was to analyze the characteristics of the ERE for students with VI enrolled in elementary school II and high school, during the pandemic. This is a descriptive exploratory research, in which data collection was carried out online, through interviews with a semi-structured script, via Google Meet. The collected data were submitted to textual content analysis, interpreted and organized into categories. Information was obtained on how students with low vision accessed content in remote classes, which platforms were more and less accessible, how teachers acted and remote work, using Google Meet as the main platform for accessing classes. The data allow us to broaden the understanding of the experiences of regular class teachers, Special Education teachers and students with low vision about ERT during the Coronavirus pandemic.

Asymmetrical portrayal of gender within professional, recreational, and domestic spaces in locally developed Pakistani English Language Textbooks: A multimodal analysis

Language textbooks often include visual elements such as images that contribute significantly to the construction of gender ideologies which play a crucial role in shaping the worldview of language learners towards gender. While prior critically oriented studies on gender have contributed to our understanding of discourses that reproduce gender bias in the textual content of English language textbooks (ELT), there has been limited attention to multimodal features in the depiction of gender. Adopting van Leeuwen's socio-semantic inventory, the current study aims to contribute to this niche area by conducting a multimodal examination of gender portrayal in secondary-level English textbooks used across four provinces of Pakistan. Our findings indicate that both text and images contribute to the asymmetrical portrayal of gender within professional, recreational, and domestic spaces. These results underscore the need for a critical review of the textbooks by Pakistani ELT educational authorities in promoting a more balanced and equitable representation of gender in classroom materials.

Design specifications and capabilities of a 3D simulation software for clinical education of physiotherapy students in the neurology department

IntroductionIn this revolutionized era, thanks to cutting-edge technological breakthroughs like 3-dimensional (3D) computerized environments, physiotherapy trainers can improve their knowledge and confidence by using such training tools. Hence, there is room for developing these technologies for training medical students to expand their skills and expertise. This study aims to identify the design requirements and key functionalities of a 3D simulation software for the clinical education of physiotherapy students in neurology departments.MethodFirst, by carefully reviewing neurological books, scientific articles, curriculum, and medical records, and consulting with experts, a scenario was compiled. In the next step, a researcher-developed questionnaire was designed. Then, experts’ opinions were considered to confirm the validity and reliability of the questionnaire. The designed questionnaire was distributed among several neurological physiotherapists. Finally, the information elements, contents, and functional capabilities of the 3D software were determined by analyzing the data obtained from the questionnaire.FindingsThe main components for the design of physiotherapy educational software were identified based on the findings of the literature review, curriculum analysis, and medical record review. A survey of physiotherapy professors was conducted using a questionnaire created by the researcher in order to enhance the capabilities of simulation software and ascertain its primary functions. Following an analysis of the data from the distributed questionnaire, 37 essential features and contents have been proven to be more crucial than the rest for the creation of 3D simulation software. As a result, the essential and fundamental needs for the patient’s training in reading their medical records and performing muscle strength assessments were recognized and extracted. Based on these findings, a researcher-developed scenario for the various real cases was then established. In the patient’s medical record reading scenario, the student is required to read the patient’s record in text format. Similarly, in the section on cranial nerves, pictures are utilised to reinforce the student’s assessment skills in addition to textual content. Together with the audio and pop-up texts, the simulated 3D environment also offers training for the assessment of muscle strength.ConclusionAs an educational tool, this software can enhance students’ learning and assist in addressing the drawbacks of conventional teaching methods like lectures and hospital visits.

Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing

With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy.

Analysis of interjections in Supporting Characters in the Novel “Rindu” by Tere Liye

The use of Interjection in supporting supporting characters in the novel "Rindu" by Tere LiyeThe method used is qualitative descriptive. The subject is the author, and the object is the Suppport character’s.The primary data source for this study is the novel "Rindu" by Tere Liye. The data is that all interjections used by the support characters in the text will be identified and analyzed.A purposive sampling technique..Instruments Used in Research: Author, Novel "RIndu" by Tere Liye, and Notebook, table (datasheet). Data collection tools are first, textual content: Tere Liye's novel. The data collection process involves several steps: Identification: Reading the novel to identify all instances of interjections used by the supporting character, Cataloging and classification Data Analysis Procedures are: 1. Identification of Interjections, 2.Classification of Interjections, 3. Contextual Analysis, 4.Frequency Analysis, and 5. Function Based on the results of the research, the most interjection words are the type of greeting and Farewell. Based on the results the type of use of greetings and farewells interjection is the most in total, as many as 30 interjections by supporting character's : Daeng andipati wife, Anna, Elsa, etc. Meanwhile, the figure who uses greetings and farewells the most is Anna with 15 word. The novel "Rindu" by Tere Liye employs interjections effectively to build the characters and enhance the emotional depth of the story. Here are several ways interjections contribute to character development in the novel: Expressing Emotions, Revealing Personality, Creating Realistic Dialogue, Indicating Cultural Background, Enhancing Tension and Drama, and Character Relationships.

Wine tasting notes as formulaic texts

The linguistic term “formulaic language” refers to fixed expressions or sequences of words that are often used as a single unit and are typically stored in memory and retrieved as complete units during communication, rather than being constructed or analyzed anew each time by the rules of grammar. Today, however, the concept of formulaicity has expanded beyond individual word combinations to include larger units as formulaic texts. In certain text genres such as abstracts and recipes, formulaicity is visible not only at the level of wording, but also in text content, structure, and layout. But it has not been examined in other text genres where professionals could benefit from this fixed structure in order to produce acceptable texts. In the present article we aim to analyze if wine tasting notes can be considered formulaic in nature. To meet our expectations, a corpus of wine tasting notes written originally in English will be used and collocations that help build up a text examined; by using Biel’s text-organizing patterns and term-embedding collocations, and by analyzing tasting notes in terms of Gülich’s criteria for formulaic texts, the present study will try to show if wine tasting notes are indeed formulaic. Our findings enhance dictionary entries by capturing the full spectrum of sensory experiences and professional terminology in oenology, improving lexical entries in both oenology and general monolingual dictionaries through the identification and categorization of formulaic language in wine tasting notes.

Thinking critically about scientific information generated by ChatGPT

Purpose In November 2022, the commercial company, OpenAI, launched ChatGPT. Since then, university students have rapidly become regular users of this artificial intelligence (AI) platform. One reason for this is the powerful capability of this generative AI tool to produce textual content, which in many cases, is almost indistinguishable from human-generated content. Another reason is that ChatGPT easily gives anyone access to knowledge. However, there is a problem as the vast majority of its users have no idea how this AI platform works and thus overlook the importance of thinking critically about information communicated in ChatGPT. While some call for banning this generative AI tool, this study aims to provide evidence that science classrooms can become scenarios where students find explicit, concrete, and realistic opportunities to critically evaluate scientific information generated by ChatGPT. Design/methodology/approach An intervention study was conducted with 55 students (26 females and 29 males, 17–24 years old) during a university Spanish-English bilingual science course taught within an active learning environment. The data consist of the written critiques of the students about Spanish-English bilingual scientific texts produced by ChatGPT. Findings Results indicate that the intervention had a positive effect on students’ abilities to construct sound arguments in Spanish and in English while judging the quality of scientific texts produced by this AI bot. Moreover, the findings suggest that the intervention enriched students’ skills to make improvements to texts produced by this generative AI tool. Originality/value The evidence provided in this study contributes to the exploration of possibilities to help students become critical users of ChatGPT.