The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack sys...The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.展开更多
People spend most of their time communicating their thoughts, ideas, attitudes, and emotions on social media platforms like Twitter, however, an important mode of communication as the nonverbal component which require...People spend most of their time communicating their thoughts, ideas, attitudes, and emotions on social media platforms like Twitter, however, an important mode of communication as the nonverbal component which requires visual and audible cues is not allowed due to the nature of these text-based platforms. The aim of this research is to discover the alternative ways Arabs use across different dialects to compensate for the absence of the nonverbal component. To be able to discover that the researchers collected a corpus of tweets written in the Arabic language by using python through the Twitter application programming interface (API). The results can be summed up as follows: emojis helped Arabs to communicate their facial expressions and the top used emoji across the different dialects was Face with Tears of Joy, it was also apparent that the top used emojis reflected the universal emotions, regarding the usage of hand gestures, Egyptian dialect came in the first place and Emirati dialect in the second place. Prosodic features such as the tone and loudness of the voice are expressed by the mean of character repetition, Punctuation usage across the Arabic dialects was limited, and Lebanese seemed to use them the most, Arabs tend to replace punctuation marks with emojis, finally, Arabs used vocal expressions like Interjections to communicate their affective state.展开更多
Using the multimodal metaphor theory,this article studies the multimodal metaphor of emotion.Emotions can be divided into positive emotions and negative emotions.Positive emotion metaphors include happiness metaphors ...Using the multimodal metaphor theory,this article studies the multimodal metaphor of emotion.Emotions can be divided into positive emotions and negative emotions.Positive emotion metaphors include happiness metaphors and love metaphors,while negative emotion metaphors include anger metaphors,fear metaphors and sadness metaphors.They intuitively represent the source domain through physical signs,sensory effects,orientation dynamics and physical presentation close to the actual life,and the emotional multimodal metaphors in emojis have narrative and social functions.展开更多
In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their ...In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their similarities and differences with facial expressions in traditional face-to-face communication.To fill this gap,we conducted a Meta-analysis with 25 independent effect sizes from previous experimental studies.The present study shows that emojis have slight advantages in processing efficiency,which might be attributed to their simplicity in design,namely the omission of complex facial features,but the difference between emoji and face processing is not significant.In addition,emotional valence and experimental methods do not have significant influences,which suggests that emojis are equally effective as human faces in emotional expression.The current research contributes to the knowledge in digital communication and the crucial role played by emojis therein.展开更多
With over a thousand emoji pictures to represent our words, let’s take a closer look at this fastgrowing language.According to Professor Vyv Evans of Bangor University, Emoji is the UK’s fastest-growing language–ev...With over a thousand emoji pictures to represent our words, let’s take a closer look at this fastgrowing language.According to Professor Vyv Evans of Bangor University, Emoji is the UK’s fastest-growing language–evolving faster than any language in history. These little electronic images started life in Japanese mobile phones in the 90s and are now hugely popular.展开更多
As the so-called'IP fever'in China’s film industry sputters to a halt,producers are hoping animation will help boost a faltering business model.Originating from China’s online culture,'intellectual prope...As the so-called'IP fever'in China’s film industry sputters to a halt,producers are hoping animation will help boost a faltering business model.Originating from China’s online culture,'intellectual property'(IP)can refer to online novels,games,or streamed entertainment that are considered marketable as mainstream media.Ahead of this year’s international Licensing Expo,held in Shanghai from July 18 to 20,展开更多
In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental asp...In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.展开更多
提出了一种基于情感分布的emoji嵌入式表示方法(emoji embedded representation based on emotion distribution,EDEER)。EDEER方法采用基于BERT的情绪预测模型软标签,从真实数据中学习emoji嵌入式表示,通过情感分布直接建模emoji在各...提出了一种基于情感分布的emoji嵌入式表示方法(emoji embedded representation based on emotion distribution,EDEER)。EDEER方法采用基于BERT的情绪预测模型软标签,从真实数据中学习emoji嵌入式表示,通过情感分布直接建模emoji在各种情绪上的表达程度,使嵌入式表示中包含emoji的多种情感信息。在包含emoji的中文微博数据集上的多组对比实验表明,本文提出的方法可以有效地学习到与细粒度情绪直接关联的emoji嵌入式表示,构建具有较高情绪表达质量的emoji表示空间。展开更多
基金funded by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University,grant number[R-2025-1637].
文摘The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.
文摘People spend most of their time communicating their thoughts, ideas, attitudes, and emotions on social media platforms like Twitter, however, an important mode of communication as the nonverbal component which requires visual and audible cues is not allowed due to the nature of these text-based platforms. The aim of this research is to discover the alternative ways Arabs use across different dialects to compensate for the absence of the nonverbal component. To be able to discover that the researchers collected a corpus of tweets written in the Arabic language by using python through the Twitter application programming interface (API). The results can be summed up as follows: emojis helped Arabs to communicate their facial expressions and the top used emoji across the different dialects was Face with Tears of Joy, it was also apparent that the top used emojis reflected the universal emotions, regarding the usage of hand gestures, Egyptian dialect came in the first place and Emirati dialect in the second place. Prosodic features such as the tone and loudness of the voice are expressed by the mean of character repetition, Punctuation usage across the Arabic dialects was limited, and Lebanese seemed to use them the most, Arabs tend to replace punctuation marks with emojis, finally, Arabs used vocal expressions like Interjections to communicate their affective state.
文摘Using the multimodal metaphor theory,this article studies the multimodal metaphor of emotion.Emotions can be divided into positive emotions and negative emotions.Positive emotion metaphors include happiness metaphors and love metaphors,while negative emotion metaphors include anger metaphors,fear metaphors and sadness metaphors.They intuitively represent the source domain through physical signs,sensory effects,orientation dynamics and physical presentation close to the actual life,and the emotional multimodal metaphors in emojis have narrative and social functions.
基金supported by Science Foundation of China University of Petroleum,Beijing(No.2462023YXZZ006)Undergraduate Key Teaching Reform Project(30GK2312).
文摘In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their similarities and differences with facial expressions in traditional face-to-face communication.To fill this gap,we conducted a Meta-analysis with 25 independent effect sizes from previous experimental studies.The present study shows that emojis have slight advantages in processing efficiency,which might be attributed to their simplicity in design,namely the omission of complex facial features,but the difference between emoji and face processing is not significant.In addition,emotional valence and experimental methods do not have significant influences,which suggests that emojis are equally effective as human faces in emotional expression.The current research contributes to the knowledge in digital communication and the crucial role played by emojis therein.
文摘With over a thousand emoji pictures to represent our words, let’s take a closer look at this fastgrowing language.According to Professor Vyv Evans of Bangor University, Emoji is the UK’s fastest-growing language–evolving faster than any language in history. These little electronic images started life in Japanese mobile phones in the 90s and are now hugely popular.
文摘As the so-called'IP fever'in China’s film industry sputters to a halt,producers are hoping animation will help boost a faltering business model.Originating from China’s online culture,'intellectual property'(IP)can refer to online novels,games,or streamed entertainment that are considered marketable as mainstream media.Ahead of this year’s international Licensing Expo,held in Shanghai from July 18 to 20,
文摘In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.
文摘提出了一种基于情感分布的emoji嵌入式表示方法(emoji embedded representation based on emotion distribution,EDEER)。EDEER方法采用基于BERT的情绪预测模型软标签,从真实数据中学习emoji嵌入式表示,通过情感分布直接建模emoji在各种情绪上的表达程度,使嵌入式表示中包含emoji的多种情感信息。在包含emoji的中文微博数据集上的多组对比实验表明,本文提出的方法可以有效地学习到与细粒度情绪直接关联的emoji嵌入式表示,构建具有较高情绪表达质量的emoji表示空间。
