Meaning In Context Research Articles

Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification

Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive or negative comments. This task becomes even more difficult when dealing with large amounts of data, making sentiment classification necessary. Automating sentiment classification involves text classification processes, commonly performed using deep learning methods. The classification process using deep learning models is closely tied to text representation. This step is critical as it affects the quality of the data being processed by the deep learning model. Traditional text representation methods often overlook the contextual meaning of sentences, leading to potential misclassification by the model. In this study, we propose a novel fusion text representation model, GloWord_biGRU, designed to enhance the contextual understanding of sentences for sentiment classification. Firstly, we combine the advantages of GloVe and Word2Vec to obtain richer and more meaningful word representations. GloVe provides word representations based on global frequency statistics within a large corpus, while Word2Vec generates word vectors that capture local contextual relationships. By integrating these two approaches, we enhance the quality of word representations used in our model. During the classification stage, we employ biGRU, considering the use of fewer parameters, which consequently reduces computational requirements. We evaluate the proposed model using the IMDB dataset. Several scenarios demonstrate that our proposed model achieves superior performance, with an F1 score of 90.21%.

Semantic Features of the Word Qonyr (Brown): Conceptual Analysis of Two Separate Works

It is known that the Aesopian language was used in the works of many writers to convey the parable. This article shows how and why Kazakh writers used the Aesopian language during the Soviet era. The study analyzes the contextual and symbolic meanings of the word qonyr, one of the most important concepts of Kazakh cognition. The research material was taken from O. Bokey’s novel Kajdasyn, Kaska Kulynym? (Where Are You, My Foal with a Star on His Forehead?), and a poem by Zhumeken Nazhimedenov titled “Kyran-Kiya” (“Mountain Eagle”). A conceptual research method in modern linguistics was chosen for data analysis. As a result, new implicit meanings of the word qonyr (“brown”) were revealed, such as “longing”, “sadness”, and “nobility”. The juxtaposition of these meanings with Soviet ideology is presented. The results and conclusions of the study are a kind of contribution to studies in cognitive linguistics and decolonization.

ILLOCUTIONARY SPEECH ACTS IN TAYLOR JENKIS REID’S NOVEL MALIBU RISING

This research aims to identify the types of illocutionary speech acts found in the main characters' utterances in Taylor Jenkins Reid's novel Malibu Rising. Using Searle’s theory of illocutionary acts, the study examines the contextual meanings behind these acts. A qualitative descriptive methodology was applied, focusing on character dialogues as data. Data collection involved reading and analyzing dialogues, selecting utterances containing illocutionary acts. Analysis followed Miles and Huberman’s framework, encompassing data reduction, data display, and conclusion/verification. Results reveal that the main characters' utterances incorporate five types of illocutionary acts: representatives, directives, declaratives, commissives, and expressives, with directives and expressives being predominant. The research underscores the role of competitive and conflicting speech acts in character interactions, demonstrating how these acts advance the narrative and depict social dynamics. Findings suggest that illocutionary acts in Malibu Rising illustrate internal states, facilitate social interactions, and navigate interpersonal relationships. This study offers insights into the pragmatic functions of language in literature, enhancing understanding of how speech acts reflect real-life communication and contribute to character development and narrative structure. Future research could build on these findings by examining illocutionary acts in various literary genres and comparing their use across different authors and themes.

Translating English Articles from Mongolian: A Case Study of Natsagdorj’s Shuvuun Saaral

This study investigates the complexities involved in translating English articles into Mongolian, focusing on Natsagdorj D.’s seminal work, Shuvuun Saaral. The absence of articles in Mongolian presents significant challenges for translators, educators, and learners, necessitating sophisticated linguistic strategies to convey intended meanings accurately. By analyzing sentences from the Mongolian novel translated into English by Western Washington University, this study explores the translation of indefinite, definite, and zero articles. The theoretical framework integrates Hawkins' (1978) referential or location theory and Bickerton's (1981) language roots theory. This study provides insights into contextual meaning conveyance, elucidates how English definite articles are translated into Mongolian, and interprets the strategies employed by translators, along with the challenges and opportunities in article translation.

Voluntary musical imagery in music practice: contextual meaning, neuroscientific mechanisms and practical applications

Practice is acknowledged as a crucial facilitator for musicians to achieve performance excellence. Despite the rich literature on incorporating musical imagery intentionally to improve one’s practice efficacy, limitations remain in the understanding of voluntary musical imagery (VMI) in the context of musical practice. Therefore, our aims in this review are threefold. First, we enriched the interpretation of VMI in the context of music practice through the lens of embodied cognition. Second, we integrated neuroscientific findings to elucidate how the deliberate use of musical imagery parallels physical practice in effectiveness. Third, we synthesize work on the application of VMI in enhancing musical learning from both theoretical and practical perspectives. By providing an integrated overview of voluntary musical imagery, we highlight gaps in the literature and encourage further research on (1) the impact of embodied experiences on VMI formation, (2) optimal imagery content and ratio combination to establish a personalized intervention protocol for more effective musical pedagogy, and (3) on physiological measures to access VMI effectiveness. Additionally, we highlight the crucial implications of VMI for researchers, performers, and music educators.

Metaphorical Idioms in Extended Conceptual Metaphor Theory

Abstract In Extended Conceptual Metaphor Theory (Kövecses, 2020), I offered a comprehensive overhaul of “standard” conceptual metaphor theory. The present paper attempts to demonstrate how the new view of CMT can handle metaphorical idioms. To this end, I analyze four metaphorical idioms from the thematic area of money (throw money about, money slips through someone’s fingers, be a cash cow, money keeps someone/something afloat). The analysis assumes and starts out from the observation that money-related metaphors are based on two generic-level conceptual metaphors: money is a moving entity and money is a force. Extended CMT adds to CMT the notion of “mental space-level metaphors” that were largely ignored in “standard” CMT, but are given an important role in extended CMT. These are the metaphors that represent actual, contextual meanings in a metaphorical usage event. Traditional CMT-type analysis cannot account for the emergence of such idiomatic meanings because the mappings, or correspondences, of standard CMT work on a single, generic level (frame-, domain-, or even image schema level). However, the contextual meanings of naturally used metaphors (including those of metaphorical idioms) are much more information-rich and specific. The conceptual metaphors on the image schema, domain, and frame levels are offline structures in long-term memory, whereas the conceptual metaphors on the mental space level occur only online in working memory. In online communication, speakers mobilize the static image schema-, domain-, and frame-level metaphors at the mental space level, where they create highly specific mental space-level metaphors. Given this framework, it becomes possible to explain how and why the four metaphorical idioms have different contextual meanings (as represented by different mental space-level metaphors), but, at the same time, why they also share certain conceptual metaphors on the frame-, domain-, or image schema-level. Additionally, we gain new insight into how the emergence of novel metaphorical idioms occurs with the help of and constrained by a previously existing large system of hierarchically arranged conceptual metaphors.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

THE ROLE OF AMERICAN STORIES IN TEACHING ENGLISH

This study examines the effectiveness of using American stories in ESL education to enhance cultural understanding, vocabulary, listening, and pronunciation skills. By engaging with American folktales and literature, students gain exposure to idiomatic expressions, cultural nuances, and diverse language structures within meaningful contexts. Storytelling reduces language anxiety, promotes critical thinking, and provides practical language use models. Suggested classroom applications include storytelling circles, role-playing, and thematic discussions, which collectively foster cultural literacy, engagement, and language proficiency.

Effectiveness of Experiential Learning Approaches (ELA) in enhancing students’ active participation in physical education

Background: Active participation in physical education (PE) is essential for students’ development yet remains challenging in many schools. Research Objectives: This study aimed to assess the effectiveness of experiential learning approaches, including role-playing and interactive simulations, in enhancing students’ willingness and ability to actively participate in PE at Gabas Integrated School (GIS). Methods: This study utilised a one-group pretest-posttest design in which 34 Grade 9 GIS students participated. A researcher-made survey questionnaire patterned from the Students’ Participation Survey and the Motivation in Physical Education Survey and a checklist tool were used to gather data. Education experts reviewed the survey questionnaire to ensure content validity and conducted a pilot test. The collected data were analysed using Jeffrey’s Amazing Statistics Program (JASP). Findings/Results: The study revealed that the students’s willingness to participate was initially moderately willing, which increased to extremely willing after the intervention. The student’s ability to participate improved from a participative rating to a highly participative rating after the intervention. The Wilcoxon signed-rank test further revealed significant differences between the students’ willingness and ability to participate before and after the intervention. Conclusion: These findings concluded that an experiential learning approach effectively enhances students’ active participation in terms of their willingness and ability to participate in PE classes. Thus, the Department of Education must encourage teachers to use ELA to transform students from passive spectators to active participants. Physical education teachers must also consider incorporating performance-based learning and real-life scenarios that allow students to apply their skills in meaningful contexts to their physical education teaching.

DIRECT AND INDIRECT SPEECH ACT IN OPRAH WINFREY’S MOTIVATIONAL SPEECH “LIFE ADVICE WILL CHANGE YOUR FUTURE”

This research was designed in descriptive qualitative research. The aims of the research were to find out direct and indirect speech act and the contextual meaning in Oprah Winfrey’s Motivational Speech “Life Advice Will Change Your Future”. In collecting the data, the researcher used content Analysis technique. The researcher used Miles and Huberman theory in analyzing the data. In doing the research, the theory of Finegan is used. Finegan stated that there were two types of speech act, direct and indirect speech act and it can be in the form of representatives, commissives, directives, declarations, expressives and verdictives. The result shows that there were found 30 expression of direct speech act and 26 expression of indirect speech act. Therefore, speech act can be found in many discourses and daily life conversation. The researcher expected that this research will be useful for teacher, students, and the next researcher who are going to do related research.

LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction

The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into human-readable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2.

WC-SBERT: Zero-Shot Topic Classification Using SBERT and Light Self-Training on Wikipedia Categories

In natural language processing (NLP), zero-shot topic classification requires machines to understand the contextual meanings of texts in a downstream task without using the corresponding labeled texts for training, which is highly desirable for various applications. In this article, we propose a novel approach to construct a zero-shot task-specific model called WC-SBERT with satisfactory performance. The proposed approach is highly efficient since it uses light self-training requiring target labels (target class names of downstream tasks) only, which is distinct from other research that uses both the target labels and the unlabeled texts for training. In particular, during the pre-training stage, WC-SBERT uses contrastive learning with multiple negative ranking losses to construct the pre-trained model based on the similarity between Wiki categories. For the self-training stage, online contrastive loss is utilized to reduce the distance between a target label and Wiki categories of similar Wiki pages to the label. Experimental results indicate that compared to existing self-training models, WC-SBERT achieves rapid inference on approximately 6.45 million Wiki text entries by utilizing pre-stored Wikipedia text embeddings, significantly reducing inference time per sample by a factor of 2,746 to 16,746. During the fine-tuning step, the time required for each sample is reduced by a factor of 23–67. Overall, the total training time shows a maximum reduction of 27.5 times across different datasets. Most importantly, our model has achieved state-of-the-art (SOTA) accuracy on two of the three commonly used datasets for evaluating zero-shot classification, namely the AG News (0.84) and Yahoo! Answers (0.64) datasets. The code for WC-SBERT is publicly available on GitHub, 1 and the dataset can also be accessed on Hugging Face. 2

Using bundle analysis to uncover formulaic language patterns in seminar discussions

Seminars serve as a valuable instructional tool in higher education, enabling scholars to engage in discussions, ask questions, and gain a clear account of the topic through interactive sessions. This leads to the significance of deciphering the discursive patterns and linguistic choices made by speakers in these academic discussions, one of these patterns is lexical bundles. This study focused on identifying the most frequently used four-word lexical bundles in 78 seminar discussions sourced from three online repositories. Drawing on Biber et al.’s taxonomy, the identified bundles underwent functional categorisation. This involved examining their contextual meanings through concordance lines to reveal something of the discoursal and communicative practices inherent in this genre, and explore how speakers organise their discourse in a formulaic way. The findings demonstrate the prominent presence of formulaic bundles in seminar discussions. Stance, which relates to the speaker’s degree of knowledge, their attitude and judgments, emerged as the most salient function of bundles, followed by referential and discourse-organising functions. These findings contribute to a deeper understanding of discourse decisions in seminars, and offer insights into the formulaic linguistic choices made by speakers as they interact with their audience.

Issues in Machine Translation with reference to the Story “Thakur ka kuan” by Munshi Premchand

This paper explores the issues encountered in machine translation, specifically using Google Translate, in translating Munshi Premchand's short story Thakur Ka Kuan ("Thakur's Well"). The study identifies key challenges, including the misinterpretation of cultural references, idiomatic expressions, and contextual meaning that are integral to Premchand's narrative style. Machine translation often produces literal translations that lack the nuance and emotional depth present in the original text, leading to misrepresentation of the story’s themes. For instance, socio-cultural terms and metaphors central to the story's message are frequently mistranslated or omitted entirely. The translation also struggles with maintaining consistency in tense and sentence structure, further diluting the narrative. This paper argues that while machine translation offers convenience for basic translations, it remains insufficient for complex literary works, where cultural, emotional, and contextual understanding is crucial. A comparative analysis with a human-translated version highlights these deficiencies and suggests that human input remains essential for accurate literary translations. The findings underscore the need for improved machine learning models to address these linguistic and cultural intricacies.

Eye closures in spoken Hebrew

Abstract The present study focuses on eye closure (EC) as a communicative facial gesture in Israeli Hebrew media talk and pays particular attention to its coordination with co-expressive verbal, prosodic, and embodied resources. Drawing on the interactional approach to language and embodied action, the study demonstrates that EC can convey four contextual meanings — concentration, hedging, negation, and totality — depending on the context in which it occurs and the verbal material with which it is co-produced. The paper proposes that these contextual meanings are derived from the more general meaning of disengagement conveyed by EC. Moreover, the present paper suggests several potential semiotic connections between the basic physiological use of EC as a reflex of self-protection and evasion and its observed contextual meanings. This provides evidence for the co-optation of the basic functions of facial gestures for communicative purposes via metaphorical-metonymical extensions.

Using boundary objects to enhance learning in history and develop students’ numeracy capabilities

ABSTRACT Informed citizenship in the 21st Century requires individuals to be numerate. In Australia, all teachers are expected to attend to numeracy inherent in the subjects they teach. Many teachers, however, find this approach challenging and may not see potential benefits for subject learning. Drawing on lesson vignettes and post-lesson interviews, a conceptual approach based on Wenger’s communities of practice was used to analyse the use of boundary objects by four secondary history teachers. Their use of boundary objects was categorised by their attention to the contextual and mathematical meanings of the artefact they used. Findings suggest that there is potential for teachers to concurrently enhance subject learning and develop students’ numeracy capabilities if they attend to both the contextual and mathematical meanings of the boundary objects they use. Research is needed into how numeracy is addressed in curriculum pedagogy courses. One aspect worthy of investigation is how boundary objects can be used effectively in subjects across the curriculum to enhance subject learning and promote numeracy development.

Digitising a Card Game for Teaching Pragmatic Markers

Most language learning applications focus on pronunciation, vocabulary, and grammar of a language. However, learning the pragmatics enables learners to interpret contextual meanings of specific words uttered by native speakers during conversations. An educational card game called Mind You! was developed for non-native English-speaking international students in Australia to help them learn English pragmatic markers. The card game was designed to encourage social constructivism and situated learning, where learners construct their own sentences based on their combined interpretations of pragmatic markers, images and scenarios displayed on cards to answer hypothetical questions. A mobile application of the card game is being developed as a supplementary resource when face-to-face or in-person learning is neither possible nor practical. Learners can listen to audios of pragmatic markers in sentences and type or record their own sentences based on their interpretations of the cards and share them online with other learners. This provides a platform for distance collaborative learning between other language learners where they share sentences based on different interpretations of different card combinations. The objective of the mobile application is to train learners in using pragmatic markers for English conversations outside the game. This work-in-progress manuscript outlines how the card game can be digitised into a mobile application.

THE ROLE OF THEME-BASED APPROACH IN ENHANCING YOUNG LEARNERS’ SPEAKING SKILLS IN PRIMARY EDUCATION

This article highlights the effectiveness of implementing theme-based model of CBI (content-based instruction) in developing young learners’ speaking skills, as well as it offers an effective way of practising a new language in a meaningful and memorable context. We know that teaching speaking in English in primary education is one of the most demanding, at the same time challenging and rewarding jobs, and young learners are “me”-focused learners, respond emotionally to learning activities. Therefore, teachers should know how to create a warm, friendly English atmosphere in the classroom, how to use different types of activities and how to set up activities in order to enable primary school children to work together in groups. Moreover, this article is based on the study conducted with primary school children to identify the effectiveness of using theme-based CBI in teaching speaking, as well as enhancing young learners’ speaking skills.

Regional CCUS strategies in the context of a fully decarbonized society

Global strategies to combat climate change must be implemented through actions at the local level. The complexity and interconnection of the global challenges are, however, difficult to interpret at the local level. This requires enhanced collaboration and coordination together with a shared understanding of the interconnected nature of our energy systems and the strategies that shape them. Global, international, and national strategies for carbon capture utilization and storage (CCUS) have emerged in recent years as carbon is increasingly outlined as a key element in the future energy system. This emphasizes CCUS strategies as an important part of a transition toward a fully decarbonized society. Therefore, principal strategies for CCUS and the energy system need to be coordinated and translated following a spatiotemporal assessment to enable meaningful actions at the local level. This article applies a regional perspective, in which a regional energy system model seeks to bridge the gap between national and local energy planning. The regional energy system is developed as part of an overarching national scenario for the transition towards a fully decarbonized society. This context provides a national boundary shaped by a global ceiling to provide a meaningful and adequate framework for regional and local development in the future. The overarching national scenario frames the potentials and constraints of the regional energy system and CCUS strategies as part of a global transition while enabling investigations of potential ramifications from changes in national objectives. The analysis in the article uses the North Denmark Region as a case and investigates scenarios in which the region assumes various roles as a contributor to the national transition. The results indicate a need for more interactive and dynamic energy planning and CCUS strategies to comprehend possible impacts from the various roles and changes in the national objectives. This article suggests that spatial and temporal awareness at the regional level of CCUS strategies as part of a future fully decarbonized society is key to creating a meaningful context for local actions today.

A Comparative Analysis of MultinomialNB, SVM, and BERT on Garuda Indonesia Twitter Sentiment

This study investigates customer sentiment towards Garuda Indonesia Airlines (GIA) using sentiment analysis of Twitter data. The research aims to identify prevailing sentiments, uncover common themes in customer feedback, and provide recommendations for improving customer satisfaction and brand loyalty. A dataset of 1,250 tweets from March 2007 to July 2024 was collected and pre-processed, including cleaning, language detection, and tokenization. Sentiment analysis was conducted using three models: MultinomialNB, SVM, and BERT.The results indicate that BERT outperformed both MultinomialNB and SVM in sentiment classification accuracy, achieving 75.6%. This highlights the effectiveness of BERT in capturing contextual meaning within customer reviews. The findings of this research will contribute to a deeper understanding of customer sentiment towards GIA and inform strategies for enhancing customer experience and brand image.