Emotion Detection Research Articles

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

Emotional Intelligence in Voice Assistants : Advancing Human-AI Interaction

This article explores the integration of emotional intelligence (EI) into AI voice assistants, examining techniques for emotion recognition from speech, adaptive response generation, and the impact on user experience. It discusses key components including acoustic feature analysis, machine learning approaches, and multimodal systems for emotion detection. The article also addresses ethical considerations such as privacy concerns, potential manipulation, and technical challenges in achieving robust, real-time, and culturally-adaptive EI systems. Applications in mental health support and customer service are examined, highlighting both the potential benefits and necessary precautions. Through a comprehensive review of recent research, this work aims to contribute to the responsible development of emotionally intelligent AI voice assistants that enhance human-AI interaction while prioritizing user well-being.

Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation

With the advancement of Artificial Intelligence (AI) and the Internet of Things (IoT), research in the field of emotion detection and recognition has been actively conducted worldwide in modern society. Among this research, speech emotion recognition has gained increasing importance in various areas of application such as personalized services, enhanced security, and the medical field. However, subjective emotional expressions in voice data can be perceived differently by individuals, and issues such as data imbalance and limited datasets fail to provide the diverse situations necessary for model training, thus limiting performance. To overcome these challenges, this paper proposes a novel data augmentation technique using Conditional-DCGAN, which combines CGAN and DCGAN. This study analyzes the temporal signal changes using Mel-spectrograms extracted from the Emo-DB dataset and applies a loss function calculation method borrowed from reinforcement learning to generate data that accurately reflects emotional characteristics. To validate the proposed method, experiments were conducted using a model combining CNN and Bi-LSTM. The results, including augmented data, achieved significant performance improvements, reaching WA 91.46% and UAR 91.61%, compared to using only the original data (WA 79.31%, UAR 78.16%). These results outperform similar previous studies, such as those reporting WA 84.49% and UAR 83.33%, demonstrating the positive effects of the proposed data augmentation technique. This study presents a new data augmentation method that enables effective learning even in situations with limited data, offering a progressive direction for research in speech emotion recognition.

Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.