Emotion Detection Research Articles

Deep convolutional neural network architecture for facial emotion recognition

Facial emotion detection is crucial in affective computing, with applications in human-computer interaction, psychological research, and sentiment analysis. This study explores how deep convolutional neural networks (DCNNs) can enhance the accuracy and reliability of facial emotion detection by focusing on the extraction of detailed facial features and robust training techniques. Our proposed DCNN architecture uses its multi-layered design to automatically extract detailed facial features. By combining convolutional and pooling layers, the model effectively captures both subtle facial details and higher-level emotional patterns. Extensive testing on the benchmark Fer2013Plus dataset shows that our DCNN model outperforms traditional methods, achieving high accuracy in recognizing a variety of emotions. Additionally, we explore transfer learning techniques, showing that pre-trained DCNNs can effectively handle specific emotion recognition tasks even with limited labeled data.Our research focuses on improving the accuracy of emotion detection, demonstrating the model’s capability to capture emotion-related facial cues through detailed feature extraction. Ultimately, this work advances facial emotion detection, with significant applications in various human-centric technological fields.

Gender Differences in Acoustic-Perceptual Mapping of Emotional Prosody in Mandarin Speech

Abstract The discrepancies in existing literature regarding the gender/sex effect on voice-emotion mapping have left the nature of the cross-gender differences unclear. To enrich the knowledge of gender differences in acoustic-perceptual mapping in emotional communication, the present study employed an acoustic-integrated approach to investigate how Mandarin speech prosody is perceived by male and female listeners. One hundred native Mandarin participants recognized the affective states and rated the emotional intensity for 4,500 audio files conveying five basic emotional prosody (i.e., anger, joy, sadness, fear, neutrality) from a female speaker. The results showed that females generally identified emotions more accurately and rated them relatively lower in intensity than males. Meanwhile, acoustic-perceptual analysis revealed a higher predictive power of acoustic measures on male performance. The research extends previous findings by showing a general female advantage in emotion detection, especially in high-arousal emotions like anger, joy, and sadness. The current study suggested that the female sensitivity to minimal affective cues should be sourced to a high-level enhancement with a subjective empathetic filter instead of a low-level superiority on objective acoustic sensation. The complicated mechanism of gender differences in emotional communication and the importance of explaining recognition ability with an acoustic-integrated perspective are highlighted.

Emotion Analysis from Face and Speech: A Comprehensive Review of Current Techniques and Models

Emotion detection in facial expressions and speech plays a crucial role in enhancing interactive platforms, particularly in learning and assessment systems. This study explores advanced techniques for integrating dynamic mock test generation and interview simulation modules in an Advanced Placement Preparation Platform. The dynamic mock test uses real-time feedback to recommend questions based on a student’s performance, leveraging machine learning algorithms for adaptive learning. Additionally, the interview simulation module incorporates facial expression recognition using Convolutional Neural Networks (CNNs) and speech analysis using Recurrent Neural Networks (RNNs) to evaluate student performance. Initial results show that traditional models struggle with real-time adaptability and emotion classification accuracy, underscoring the need for specialized algorithms for complex data. To address these limitations, the system evaluates deep learning models designed for adaptive learning and emotion analysis, such as transfer learning models in emotion detection. The findings highlight the potential for using multimodal data to improve user engagement and performance evaluation in educational settings, paving the way for more immersive and intelligent learning platforms.

Students Emotion and Distraction Detection While Adopting E-Learning Approach

Currently, e-learning has changed the way students’ study by providing high-quality education that is not restricted by place or time. Mobile phones, tablets, laptops, and desktop computers are some of the products that make online learning easier. These devices were used for mandatory online learning due to the COVID-19 pandemic. However, because the e-learning approach prevents an instructor from actively observing a group of students, they may become distracted for many reasons, significantly reducing their learning potential. This paper proposes an intelligent system called the Intelligent E-Learning Monitoring System (IELMS) that helps faculty members keep track of such students and supports them in improving their performance. Convolutional neural network (CNN) techniques are utilized to detect emotions, and once the optimum algorithm for detecting emotions has been identified, it is fused into the model that detects an online learner’s distraction. The fused model produces logs of distraction and emotion. These logs will assist the teaching community in identifying underperforming online learners and facilitating counseling.

Emotion detection from facial images: A hybrid approach to feature extraction and classification

Emotion detection from facial images: A hybrid approach to feature extraction and classification

Semantically-Informed Graph Neural Networks for Irony Detection in Turkish

Social media plays an important role in expressing the thoughts and sentiments of users. Irony is a way of stating a sentiment about something by expressing the opposite of the intended literal meaning. Irony detection is a recent emerging task in low-resource languages, although other tasks related to sentiment, such as sentiment analysis and emotion detection, have been widely tackled. In this study, we investigate Graph Neural Networks (GNNs) for irony detection in Turkish, a low-resource language in sentiment-related tasks. We incorporate semantic information into the GNNs using the Universal Conceptual Cognitive Annotation (UCCA) framework. Extensive experimental results and in-depth analysis show that our models outperform state-of-the-art irony detection models in Turkish. Our UCCA-GAT (UCCA-Graph Attention Network) model achieves an F 1 -score of 94.85% (7.362% gain over the state-of-the-art) on the Turkish-Irony-Dataset and an accuracy of 72.82% (4.39% gain over the state-of-the-art) on the IronyTR Dataset. We also provide a comprehensive analysis of the proposed models to understand their limitations.

Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN)

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

Effects of Facial Recognition and Text Semantic Recognition on Affective Tutoring System

Facial recognition is the fastest method to detect emotions, and it can be combined with text semantic recognition to increase the accuracy of emotion detection. Emotions and creativity have often been discussed in recent years. A student will be interested in learning as long as he or she enjoys learning. Mind mapping is an active and simultaneous use of the left and right brains to stimulate the creativity and imagination of the learner, which is complementary to positive emotion. This study constructed an affective adaptive learning system that combines text emotions and facial clues in chatbot conversations to understand the emotional responses of learners during the learning process. The system is tailored to accommodate diverse learners through modules of varying difficulty scales, the incorporation of mind maps to support teaching and learning, and the implementation of asynchronous online discussion forums for discourse and feedback on course-related topics. The results revealed significant differences in critical thinking, reflective thinking, and problem-solving skills between the experimental and control groups, and students in the experimental group outperformed those in the control group on the creation of mind maps.

Change and Detection of Emotions Expressed on People’s Faces in Photos

Human emotions are an element of attention in various areas of interest such as psychology, marketing, medicine, and public safety. Correctly detecting human emotions is a complex matter. The more complex and visually similar emotions are, the more difficult they become to distinguish. Making visual modifications to the faces of people in photos in a way that changes the perceived emotion while preserving the characteristic features of the original face is one of the areas of research in deepfake technologies. The aim of this article is to showcase the outcomes of computer simulation experiments that utilize artificial intelligence algorithms to change the emotions on people’s faces. In order to detect and change emotions, deep neural networks discussed further in this article were used.

A STUDENT SENTIMENT ANALYSIS METHOD BASED ON MULTIMODAL DEEP LEARNING

Based on electroencephalography (EEG) and video data, we propose a multimodal affective analysis approach in this study to examine the affective states of university students. This method is based on the findings of this investigation. The EEG signals and video data were obtained from 50 college students experiencing various emotional states, and then they were processed in great detail. The EEG signals are pre-processed to extract their multi-view characteristics. Additionally, the video data were processed by frame extraction, face detection, and convolutional neural network (CNN) operations to extract features. We take a feature splicing strategy to merge EEG and video data to produce a time series input to realize the fusion of multimodal features. This allows us to realize the fusion of multimodal features. In addition, we developed and trained a model for the classification of emotional states based on a long short-term memory network (LSTM). With the help of cross-validation, the experiments were carried out by dividing the dataset into a training set and a test set. The model’s performance was evaluated with the help of four metrics: accuracy, precision, recall, and F1-score. When compared to the single-modal method of sentiment analysis, the results demonstrate that the multimodal approach, which combines EEG and video, demonstrates considerable advantages in terms of sentiment detection. Specifically, the accuracy obtained from the multimodal approach is significantly higher. As part of its investigation, the study also investigates the respective contributions of EEG and video aspects to emotion detection. It discovers that these features complement each other in a variety of emotional states and have the potential to improve the overall recognition results. The multimodal sentiment analysis method that is based on LSTM offers a high level of accuracy and robustness when it comes to recognizing the affective states of college students. This is especially essential for enhancing the quality of education and providing support for mental health.

Emotion detection in text: advances in sentiment analysis

Emotion detection in text: advances in sentiment analysis

Societal Impact of Machine Learning on Human Emotions

Modern networking conversations generate annotated metadata, necessitating a method for synthesizing insights from statistics. Emotion detection is crucial for practical conversations, distinguishing joy, grief, and wrath. Corpora are becoming the standard for human–machine interaction, aiming to make interactions feel natural and real. A paradigm that identifies debates and customer views can provide a human touch to these interactions. Researchers developed a machine learning framework for assessing emotions in English phrases, utilizing Long Short Term Memory perspective and real-time emotion recognition in idiomatic speech. Emotion recognition rule is created using ontologies like Word Net and Concept Net, Naive Bayes, and Random Forest. Real-time analysis of written words and facial expressions significantly outperforms current algorithms and commandment classifiers in identifying emotional states.

Retraction notice: Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

Retraction notice: Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

Personalized Emotion Detection Adapting Models to Individual Emotional Expressions

Emotion recognition from text and speech has become a critical area of research in artificial intelligence (AI), enhancing human-computer interaction across various sectors. This paper explores the methodologies used in emotion recognition, focusing on Natural Language Processing (NLP) for text and acoustic analysis for speech. It reviews key machine learning and deep learning models, including Support Vector Machines (SVM), neural networks, and transformers, and highlights the datasets commonly used in emotion detection studies. The paper also addresses challenges such as multimodal integration, data ambiguity, and ethical considerations like privacy concerns and bias in models. Applications in customer service, healthcare, education, and entertainment are discussed, showcasing the growing importance of emotion recognition in AI- driven systems. Future research directions, including advancements in deep learning, multimodal systems, and real-time processing, are also explored to address existing limitations.

Deep Learning for Text Sentiment Analysis: A Survey

Abstract. With the continuous improvement of many factors such as corpus, computing power, data scale, and laboratory conditions, how to quickly and accurately understand the emotional tendency in the text has become an important issue in many fields. The current state of affairs and the trajectory that deep learning is pursuing in the realm of text emotion analysis hold significant importance. This article first introduces the definition, importance and classification of emotional analysis, including the analysis of emotional polarity, emotional detection and fine-grained emotion. Subsequently, the key technologies of deep learning were discussed and the application of deep learning in natural language processing, especially LSTM, Bert, and their specific applications in emotional analysis within deep learning models. Then reveal the advantages and limitations of each model by comparing the performance of different deep learning models in emotional analysis tasks. Finally, in combination with current deep learning research, this study summarizes the challenges and research trends in the field of text emotional analysis.

EXPLOITING OPTIMAL SELF-ATTENTION DEEP LEARNING-BASED RECOGNITION OF TEXTUAL EMOTIONS FOR DISABLED PERSONS

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

Positioning Natural Language Processing Use for Emotion Detection from a Humanistic Perspective: A Bibliometric Study

The burgeoning use of Natural Language Processing (NLP) across diverse disciplines has significantly broadened the scope of this analysis technique. Integrating NLP into complex domains such as emotion detection (ED) brings forth the potential of NLP beyond traditional realms of sciences, extending its influence into humanistic fields like arts, humanities, and social sciences. Although several bibliometric studies have explored various implications of NLP, no significant work has focused specifically on trends in NLP within the context of ED. Thus, this study aims to investigate the global evolution and publishing trends of the intersections of NLP and ED through the Scopus database. Employing visualization tools such as RStudio and VOSviewer, this comprehensive analysis of 984 research articles, reveals publication trends from 2000 to 2023. The findings of the paper indicate the primary subject areas and leading countries with scholarly contributions. The findings also identified key themes that provide the scope for future researchers to understand the main concepts that define NLP and ED. Thus, helping readers and future scholars navigate organizing data is crucial for building strong theoretical models and practices in this rapidly growing area.

Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.

Survey on Multimodal Emotion Detection

As we know, many people suffer from unhappiness in today’s world, so there is a requirement for a system that recommends music based on human emotions such as anger, sadness, happiness, etc. In this paper, we propose a system named Multimodal Emotion Detection which helps people by suggesting music and movies based on their emotions. We implemented this system using the FER-2013 dataset, which contains 35,887 images of different emotions such as sadness, happiness, and anger. We used Python’s TensorFlow framework and Haar Cascade algorithms to recommend movies and music based on human facial emotions. The main concern in existing recommendation systems is manual sorting. To avoid this, we propose this model, which automatically plays music and movies without requiring much browsing time