Search Results (97)

The Harry Potter fandom community around the world prefers a universe of wizards and witches that includes all people, but also has concerns about the author’s perspective regarding gender identity. This disjunction paralyzes the cultural reader with moral confusion, which is a danger to their emotional investment in the text. Although scholars have analyzed this phenomenon using fragmented prisms, such as social media activism, cognitive engagement, translation, pedagogy, and fan creativity, there is no unifying model that can be used to understand why reading pleasure endures. This article aims to fill this gap by examining these strands of research in a divergent manner, adopting a convergent mixed-methods study approach. Based on neurocognitive (EEG) values, cross-cultural focus groups, social media analysis, and corpus linguistics, we outline the terrain of reader coping mechanisms. We identify separate fan fractions and examine the corresponding practices. The results are summarized by proposing a model called the MDRL (Moral dissonance repair loop) which is a theoretical model that shows how translation smoothing, pedagogical reframing and fan-based re-moralization interact with one another in creating a system that enables the reader to be collectively able to obtain their relations with the text back to a manageable point and continue being engaged. This model makes a theoretical contribution to new areas in the study of fans, moral psychology, and cognitive literature. Full article

This paper combines Christopher Alexander’s pattern language with generative AI into a hybrid design framework. The result is a narrative synthesis that can be useful for informed project design. Advanced large language models (LLMs) enable the real-time synthesis of design patterns, making complex architectural choices accessible and comprehensible to stakeholders without specialized architectural knowledge. A lightweight, web-based tool lets project teams rapidly assemble context-specific subsets of Alexander’s 253 patterns, reducing a traditionally unwieldy 1166-page corpus to a concise, shareable list. Demonstrated through a case study of a university department building, this method results in environments that are psychologically welcoming, fostering health, productivity, and emotional well-being. LLMs translate these curated patterns into vivid experiential narratives—complete with neuroscientifically informed ornamentation. LLMs produce representative images from the verbal narrative, revealing a surprisingly traditional design that was never input as a prompt. Two separate LLMs (for cross-checking) then predict the pattern-generated design to catalyze improved productivity as compared to a standard campus building. By bridging abstract design principles and concrete human experience, this approach democratizes architectural planning grounded on Alexander’s human-centered, participatory ethos. Full article

This paper presents a comprehensive review of multimodal emotion recognition (MER), a process that integrates multiple data modalities such as speech, visual, and text to identify human emotions. Grounded in biomimetics, the survey frames MER as a bio-inspired sensing paradigm that emulates the way humans seamlessly fuse multisensory cues to communicate affect, thereby transferring principles from living systems to engineered solutions. By leveraging various modalities, MER systems offer a richer and more robust analysis of emotional states compared to unimodal approaches. The review covers the general structure of MER systems, feature extraction techniques, and multimodal information fusion strategies, highlighting key advancements and milestones. Additionally, it addresses the research challenges and open issues in MER, including lightweight models, cross-corpus generalizability, and the incorporation of additional modalities. The paper concludes by discussing future directions aimed at improving the accuracy, explainability, and practicality of MER systems for real-world applications. Full article

Understanding the mindset of people who die by suicide remains a key research challenge. We map conceptual and emotional word–word co-occurrences in 139 genuine suicide notes and in reference word lists, an Emotional Recall Task, from 200 individuals grouped by high/low depression, anxiety, and stress levels on DASS-21. Positive words cover most of the suicide notes’ vocabulary; however, co-occurrences in suicide notes overlap mostly with those produced by individuals with low anxiety (Jaccard index of 0.42 for valence and 0.38 for arousal). We introduce a “words not said” method: It removes every word that corpus A shares with a comparison corpus B and then checks the emotions of “residual” words in $A-B$. With no leftover emotions, A and B are similar in expressing the same emotions. Simulations indicate this method can classify high/low levels of depression, anxiety and stress with 80% accuracy in a balanced task. After subtracting suicide note words, only the high-anxiety corpus displays no significant residual emotions. Our findings thus pin anxiety as a key latent feature of suicidal psychology and offer an interpretable language-based marker for suicide risk detection. Full article

The rapid development and deployment of artificial intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI’s ChatGPT, a large language model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT’s reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies. Full article

The adverb anymore is standardly a negative polarity item (NPI), which must be licensed by triggers of non-positive polarity. Some Englishes also allow anymore in positive-polarity clauses. Linguists have posited that this non-polarity anymore (NPAM) carries a feature of negative affect. However, this claim is based on elicited judgments, and linguists have argued that respondents cannot reliably evaluate NPAM via conscious judgment. To solve this problem, we employ sentiment analysis to examine the relationship between NPAM and negative affect in a Twitter corpus. Using two complementary sentiment analytic frameworks, we demonstrate that words occurring with NPAM have lower valence, higher arousal, and lower dominance than words occurring with NPI-anymore. Broadly, this confirms NPAM’s association with negative affect in natural-language productions. We additionally identify inter- and intra-regional differences in affective dimensions, as well as variability across different types of NPI trigger, showing that the relationship between negative affect and NPAM is not monolithic dialectally, syntactically, or semantically. The project demonstrates the utility of sentiment analysis for examining emotional characteristics of low-frequency variables, providing a new tool for dialectology, micro-syntax, and variationist sociolinguistics. Full article

Approximately 70% of people will experience a traumatic event in their lifetime, but post-traumatic stress disorder (PTSD) will only develop in 3.9% and complex post-traumatic stress disorder (CPTSD) in 1–8% of the population worldwide, although in some countries (e.g., Poland and Northern Ireland) it will develop in a much higher percentage. Stress-related disorders have a complex pathogenesis involving neurophysiological, genetic, epigenetic, neuroendocrine and environmental factors. This article reviews the current state of knowledge on the molecular aspects of selected PTSD symptoms: hypervigilance, re-experiencing, emotion dysregulation and dissociation, i.e., the symptoms with strong neurobiological components. Among analysed susceptibility factors are specific gene polymorphisms (e.g., FKBP5, COMT, CHRNA5, CRHR1, 5-HTTLPR, ADCY8 and DRD2) and their interactions with the environment, changes in the HPA axis, adrenergic hyperactivity and disturbances in the activity of selected anatomical structures (including the amygdala, prefrontal cortex, corpus callosum, anterior cingulate gyrus and hippocampus). It is worth noting that therapeutic methods with proven effectiveness in PTSD (TF-CBT and EMDR) have a substantial neurobiological rationale. Molecular aspects seem crucial when searching for effective screening/diagnostic methods and new potential therapeutic options. Full article

The focus on Speech Emotion Recognition has dramatically increased in recent years, driven by the need for automatic speech-recognition-based systems and intelligent assistants to enhance user experience by incorporating emotional content. While deep learning techniques have significantly advanced SER systems, their robustness concerning speaker gender and out-of-distribution data has not been thoroughly examined. Furthermore, standards for SER remain rooted in landmark papers from the 2000s, even though modern deep learning architectures can achieve comparable or superior results to the state of the art of that era. In this research, we address these challenges by creating a new super corpus from existing databases, providing a larger pool of samples. We benchmark this dataset using various deep learning architectures, setting a new baseline for the task. Additionally, our experiments reveal that models trained on this super corpus demonstrate superior generalization and accuracy and exhibit lower gender bias compared to models trained on individual databases. We further show that traditional preprocessing techniques, such as denoising and normalization, are insufficient to address inherent biases in the data. However, our data augmentation approach effectively shifts these biases, improving model fairness across gender groups and emotions and, in some cases, fully debiasing the models. Full article

In this paper, I investigate what role religious emotions, passions, and prayer may play in what may be called the religion of the heart in early Syrian theology, especially in the several works of ’Aphrahaṭ, Saint Ephrem, and the anonymous collection titled the Book of Steps, all from the fourth century. In the original Syriac sources, one may see how in this vast corpus, religious emotions and prayer act as a corridor channeling the believer’s striving for the disclosure of the mysteries surrounding God. It will also be shown that while these sources advocate in unison for the heart’s function in connecting the believer with God, their respective interpretive theological contexts are different. These points are substantiated by virtue of a number of citations from the original texts. Full article

The preservation of intangible cultural heritage (ICH) has transitioned from “static” and “living” approaches to a “digital ecosystem”, becoming a significant topic of anthropological research. This study, adopting an anthropological perspective, integrates sentiment semantic analysis with user identity classification to propose the Identity and Sentiment-Centered Framework for Intangible Cultural Heritage (ISC-ICH). Drawing on four types of ICH museums in Eastern Sichuan, China—Nanchong Langzhong Wang Shadow Puppetry Museum, Bazhong Pingchang Fanshan Jiaozi Base, Guang’an Eastern Sichuan Folk Museum, and Dazhou ICH Exhibition Hall—as case studies, this research analyzes the core factors contributing to the audience’s sense of local identity, including its composition, emotional needs, and cultural interaction. The findings reveal that: (1) “Explorers” and “Experience Seekers” constitute the primary audience groups, with their emotional evaluations closely tied to cultural depth and interactivity. (2) The digital transformation of ICH museums faces challenges such as resource limitations, festival-centric phenomena, the rise of “internet celebrity” trends, and technological homogenization. This paper introduces a culturally tailored corpus and a comprehensive evaluation framework, highlighting the dynamic interaction between ICH and its audience. Additionally, it proposes effective digital strategies to enhance the social and cultural identity of ICH museums in peripheral regions. Full article

Public opinion comments are important for the public to express their emotions and demands. Accordingly, identifying the public emotions contained in comments and taking corresponding countermeasures according to the changes in the emotions are of great theoretical and practical significance to online public opinion management. This study took a public opinion event at a college as an example. Firstly, the microblogs and comment data related to the event were crawled with Python coding, and pre-processing operations such as cleaning, word splitting, and de-noising were carried out; then, the stage of public opinion was divided into phases based on the daily public opinion sound volume, Baidu index, and key time points of the event. Secondly, for sentiment analysis, a supplementary sentiment dictionary of the event was constructed based on the SO-PMI algorithm and merged with the commonly used sentiment dictionary to pre-annotate the sentiment corpus; then, the RoBERTa–BiLSTM–Attention model was constructed to classify the sentiment of microblog comments; after that, four evaluation indexes were selected and ablation experiments were set up to verify the performance of the model. Finally, based on the results of the sentiment classification, we drew public opinion trends and sentiment evolution graphs for analysis. The results showed that the supplementary dictionary constructed based on the SO-PMI algorithm significantly improved the pre-labelling accuracy. The RoBERTa–BiLSTM–Attention model achieved 91.56%, 90.87%, 91.07%, and 91.17% in accuracy, precision, recall, and F1-score, respectively. The situation notification, expert response, regulatory dynamics, and secondary public opinion will trigger significant fluctuations in the volume of public opinion and public sentiment. Full article

The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic. Full article

This research responds to the increasing call to hone Student Teachers’ (ST) skill of selective attention or professional noticing as an essential early step toward developing noticing in the classroom environment. Furthering the line of research on teacher noticing via videos, this study aims to identify the distinguishing features of STs’ noticing in a Corpus Linguistics approach and enhance our understanding of STs’ habitual ways of thinking, doing, and feeling in using videos to reflect collaboratively. Participants were 40 final-year STs majoring in English Language Education and five tutors from the same university. During their 8 weeks of professional practicum, STs recorded their classes and reflected on their practice using the Self-Evaluation of Teacher Talk through Video Enhanced Observation (SETTVEO) tag set and as part of online professional learning communities. A 200,000-word Corpus of Dialogic Reflection (CoDR) was constructed and analysed using the #LancsBox 6.0 tool. Findings highlight novice teachers’ unconsciousness or problematisation of their personal agency, haste in proposing alternative practices, and cognitive or emotional dissonance when they analyse their own videos in group settings. This study has implications for the guidance needed in technology-enhanced dialogic reflection. It suggests how teacher educators could tailor their support to the reflective and professional needs of novice teachers for a more productive, transformative reflection and teacher-learning experience. Full article

Speech emotion recognition (SER) is important in facilitating natural human–computer interactions. In speech sequence modeling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of paralinguistic features to achieve unambiguous emotional semantic understanding. In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. To address these challenges, this paper proposes a novel multi-scale feature pyramid network. The enhanced multi-scale convolutional neural networks (MSCNNs) significantly improve the ability to extract multi-granular emotional features. Experimental results on the IEMOCAP corpus demonstrate the effectiveness of the proposed approach, achieving a weighted accuracy (WA) of 71.79% and an unweighted accuracy (UA) of 73.39%. Furthermore, on the RAVDESS dataset, the model achieves an unweighted accuracy (UA) of 86.5%. These results validate the system’s performance and highlight its competitive advantage. Full article

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research. Full article