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Mental health counseling is a significant challenge in contemporary society, primarily due to issues such as cost, stigma, fear, and limited availability. Emotions play a crucial role in conveying information in this context, making emotion detection essential for a deeper understanding of an individual’s mental well-being. Utilizing generative machine learning models in mental health counseling could potentially lower barriers to access and improve outcomes. This paper proposes the development of a deep learning-based emotion-detecting chatbot named Serenity. The approach involves combining a pre-trained deep neural model, RoBERTa, with a multi-resolution adversarial model, EmpDG, to enhance the accuracy of detected emotions and generate more empathetic responses. RoBERTa has been trained on a dataset of thousands of tweets from Twitter. Additionally, an interactive adversarial learning framework is introduced to leverage user feedback and assess the emotional perceptivity of generated responses in dialogues. The study aims to demonstrate that a machine learning-based mental health chatbot like Serenity has the potential to serve as an effective complement to traditional human counselors. [ABSTRACT FROM AUTHOR]

The specific purpose of this study is to test the accuracy of the YOLO method in recognizing human facial expressions through tests that involve several types of expressions such as angry, surprised, happy, neutral, and afraid. 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. Many methods 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, surprised, and disgusted facial expressions having a good level of accuracy and fearful facial expressions having a poor accuracy level. The YOLO method can detect the facial expressions of humans who wear accessories such as glasses. [ABSTRACT FROM AUTHOR]

In European studies, the most used definition of Urban Green Spaces (UGS) is based on the European Urban Atlas, which includes public green areas primarily used for recreation and green areas adjacent to urban areas that are managed or utilized for recreational purposes. UGS play a vital role in creating sustainable and resilient cities, as they provide essential social benefits for the well-being and health of urban residents. Both planners and scientists acknowledge the importance of involving, actively or passively, citizens in defining criteria for designing and managing inclusive and functional UGS. According to a post-normal science approach, the integration of hard data from scientific sources with soft data gathered from citizens' engagement holds the potential to shape an innovative support system for public policies addressing significant, urgent, and uncertain challenges pertaining to UGS. Nowadays, the abundance of data generated through online reviews, opinions, and comments allows for collecting valuable information about people's opinions and sentiments towards UGS. This study proposes a methodological framework that utilizes emotion detection techniques to identify and analyze citizens' emotions concerning UGS through social reviews. To balance computational costs and classification accuracy, the framework introduces a fuzzy emotion-based classification method called FREDoC (Fuzzy Relevance Emotions Document Classification). This method incorporates a lightweight natural language pro-cessing (NLP) approach to detect and annotate terms associated with specific emotional categories within the text. The framework adopts the psycho-evolutionary classification approach based on R. Plutchik's observations of general emotional responses. This model is implemented within a Geographical Information System (GIS) for the purpose of categorizing UGS, specifically green parks, according both to WHO report key indicators and to the detected relevant emotions. The outcome is a novel classification model of UGS that can assist decision-makers in identifying the attractiveness of UGS as catalysts for urban transformation processes. [ABSTRACT FROM AUTHOR]

Emotional facial expression detection is a critical component with applications ranging from human- computer interaction to psychological research. This study presents an approach to emotion detection using the state-of-the-art YOLOv8 framework, a Convolutional Neural Network (CNN) designed for object detection tasks. This study utilizes a dataset comprising 2,353 images categorized into seven distinct emotional expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. The findings suggest that the YOLOv8 framework is a promising tool for emotional facial expression detection, with a potential for further enhancement through dataset augmentation. This research demonstrates the feasibility and effectiveness of using advanced CNN architectures for emotion recognition tasks. [ABSTRACT FROM AUTHOR]

The digital age has resulted in a massive increase in the amount of available textual data, including articles, comments, texts, and updates on social networks. The value of analyzing such a large volume of data extends to many other industries and applications, as it provides important insights into the perspectives of customers, strategic decision-making, and market demands. Detecting emotions in texts faces challenges due to linguistic patterns and cultural nuances. This study proposes a system capable of accurately identifying emotions expressed in text using a variety of machine learning models, including logistic regression, extra randomized tree, voting, SGD, and LinearSVC. It also employs different feature extraction techniques, such as TF-IDF, Bag-of-Words, and N-grams, comparing their performance in these models. An evaluation was carried out using two English emotion datasets, namely ISEAR and AIT-2018, using F1 score, accuracy, recall, and precision. The findings demonstrate the ability and effectiveness of the system to detect emotions conveyed within texts. The LinearSVC model with N-grams achieved the highest accuracy of 88.63% on the ISEAR dataset, while the extra randomized tree classifier with N-grams achieved 89.14% accuracy on the AIT-2018 dataset. Furthermore, the SGD model with TF-IDF achieved 88.18% and 84.54% accuracy on the ISEAR and the AIT-2018 datasets, respectively. [ABSTRACT FROM AUTHOR]

Emotion detection (ED) from noisy or unstructured text data is a challenging and active area of research in natural language processing as the text contains irrelevant information like repeating characters, slang words, abbreviated words, acronyms, etc., hence text preprocessing is essential to convert unstructured text data into a structured format for any task related to text classification. The performance of the classification method is greatly affected by these preprocessing techniques. However, very limited studies evaluated the impact of these preprocessing on model performance. Hence, this paper investigate the effect of 13 commonly used techniques such as lowercasing, stemming, lemmatization, stop words removal, etc. 'on the accuracy of ED classifiers. In our experiment we apply various machine learning (ML) and deep learning (DL) classifiers such as logistic regression (LR), support vector machine (SVM), multinomial Naïve Bayes (MNB), decision tree (DT), random forest (RF), bi-directional LSTM (Bi-LSTM), bidirectional encoder representation from transformers (BERT) on amazon product review dataset to analyze the effectiveness of these techniques. Our experimental result shows that some preprocessing techniques can help in increasing the accuracy of the classifier while others have no significant impact on the classification accuracy, our study also reveals that the effectiveness of these techniques depends on the type of the selected classifier. We also evaluate the combination of the techniques and our results show that effective technique combination works better for LR, DT, and BiLSTM models. At last, based on our experimental results, the BERT model achieves the highest weighted F1_score of 97%. [ABSTRACT FROM AUTHOR]

Human-computer interaction has seen growing interest in emotion detection. To gain deeper insights into the physiological indicators of emotions, researchers have delved into utilizing electroencephalography (EEG) and micro-gestures (MGs). This study assesses the efficacy of EEG and MG features in emotion detection by recruiting 15 participants to gather EEG and MG data in response to diverse figure-based emotional stimuli. To incorporate these features, this article introduces Emo-MG, a multimodal interface that integrates EEG and MG features and employs a long short-term memory (LSTM) model to predict emotional states within the valence-arousal-dominance (VAD) space. This study presents an in-depth analysis of feature importance and correlation results based on EEG and MG features for feature selection in emotion detection tasks. Through accuracy and F1-score metrics, Emo-MG achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models, validating the efficacy of integrating EEG and MG features [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients' lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer's and Parkinson's diseases. [ABSTRACT FROM AUTHOR]

Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN with word embedding techniques introduced an additional layer to the landscape of emotion detection from text. The synergy between word embedding techniques and CNN harnesses the strengths of both approaches. CNNs extract local patterns and features from sequential data, making them well-suited for capturing relevant information within the embeddings. The results obtained with various embeddings highlight the significance of choosing synergistic combinations for optimum performance. The combination of CNNs and word embeddings proved a versatile and effective approach. [ABSTRACT FROM AUTHOR]

Emotions play a vital role in almost all the activities we perform, including learning. In fact, the success of any learning system is largely dependent upon its ability to deliver the course content in such a form so as to meet the learning requirements of the target audience. Learning Systems can be tailored to effectively utilize the feedback from learners to improve the course content, and thus the feedback can prove to be a valuable asset. There is an increased demand for focusing on a learner-centric approach to content delivery. In this study we attempt at detecting different learning-relevant emotions from the feedback for a course so as to enable course designers to incorporate the type of content that matches a learners requirements. Rather than taking into account six basic emotions (sadness, happiness, fear, anger, surprise and disgust) we consider interest, engagement, confusion, frustration, disappointment, boredom, hopefulness and satisfaction emotions for the purpose of our study since they are more relevant in a learning setup. We employed a supervised algorithm, Support Vector Machine, for affect detection from the textual feedback in our experiments. [ABSTRACT FROM AUTHOR]

Using proposed system psychologists can use technology to make decisions which can provide ease for both patients and psychologists. Psychologists can check the progress of patients by analysing emotions reports of patient over time. Using historical data and emotion detection technology psychologists can make more accurate decisions. Using proposed system patient and psychologists don't have to go to anywhere they only need a device and internet. Based on the characteristics of patient emotion psychologist only need report generated by system and prescribe medicine in emergency situation. Proposed system improves consultancy method by using machine learning emotion detection algorithm. Proposed system detects facial emotion of patient by using CNN with HAAR cascade classifier. We use FER 2013 dataset to train our model. We use VGG 19 architecture to train our model for optimization function to enhance the accuracy of model. We use RELU. We use DJANGO framework for integration with frontend. Result of our model on dataset 82.3% after find tuning the accuracy goes to 82.3% to 92%. We use recall and F1 method to check the performance of model. We trained model on the testing dataset which have gray scale images and 48*48pixel images to achieve his performance. To achieve our accuracy goal, we split dataset into trainee validation and testing dataset. We use CNN and achieve 93% accuracy in our system which help patient to get feedback only selected question and psychologist. Patients select psychologist to answer questions of psychologist system stores emotions of patient against every question to generate emotion report. Psychologist can analyze emotion report to provide better prescription to patient. [ABSTRACT FROM AUTHOR]

Using proposed system psychologists can use technology to make decisions which can provide ease for both patients and psychologists. Psychologists can check the progress of patients by analysing emotions reports of patient over time. Using historical data and emotion detection technology psychologists can make more accurate decisions. Using proposed system patient and psychologists don’t have to go to anywhere they only need a device and internet. Based on the characteristics of patient emotion psychologist only need report generated by system and prescribe medicine in emergency situation. Proposed system improves consultancy method by using machine learning emotion detection algorithm. Proposed system detects facial emotion of patient by using CNN with HAAR cascade classifier. We use FER 2013 dataset to train our model. We use VGG 19 architecture to train our model for optimization function to enhance the accuracy of model. We use RELU. We use DJANGO framework for integration with frontend. Result of our model on dataset 82.3% after find tuning the accuracy goes to 82.3% to 92%. We use recall and F1 method to check the performance of model. We trained model on the testing dataset which have gray scale images and 48*48pixel images to achieve his performance. To achieve our accuracy goal, we split dataset into trainee validation and testing dataset. We use CNN and achieve 93% accuracy in our system which help patient to get feedback only selected question and psychologist. Patients select psychologist to answer questions of psychologist system stores emotions of patient against every question to generate emotion report. Psychologist can analyze emotion report to provide better prescription to patient.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research. [ABSTRACT FROM AUTHOR]

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online social media platforms due to its easy, unique, and advanced accessibility using the API. On the other hand, supervised learning is the most widely used paradigm for tasks involving sentiment polarity and fine-grained emotion detection in short and informal texts, such as Twitter posts. However, supervised learning models are data-hungry and heavily reliant on abundant labeled data, which remains a challenge. This study aims to address this challenge by creating a large-scale real-world dataset of 17.5 million tweets. A distant supervision approach relying on emojis available in tweets is applied to label tweets corresponding to Ekman's six basic emotions. Additionally, we conducted a series of experiments using various conventional machine learning models and deep learning, including transformer-based models, on our dataset to establish baseline results. The experimental results and an extensive ablation analysis on the dataset showed that BiLSTM with FastText and an attention mechanism outperforms other models in both classification tasks, achieving an F1-score of 70.92% for sentiment classification and 54.85% for emotion detection. [ABSTRACT FROM AUTHOR]

Along with the growth of Internet with its numerous potential applications and diverse fields, artificial intelligence (AI) and sentiment analysis (SA) have become significant and popular research areas. Additionally, it was a key technology that contributed to the Fourth Industrial Revolution (IR 4.0). The subset of AI known as emotion recognition systems facilitates communication between IR 4.0 and IR 5.0. Nowadays users of social media, digital marketing, and e-commerce sites are increasing day by day resulting in massive amounts of unstructured data. Medical, marketing, public safety, education, human resources, business, and other industries also use the emotion recognition system widely. Hence it provides a large amount of textual data to extract the emotions from them. The paper presents a systematic literature review of the existing literature published between 2013 to 2023 in text-based emotion detection. This review scrupulously summarized 330 research papers from different conferences, journals, workshops, and dissertations. This paper explores different approaches, methods, different deep learning models, key aspects, description of datasets, evaluation techniques, Future prospects of deep learning, challenges in existing studies and presents limitations and practical implications. [ABSTRACT FROM AUTHOR]

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Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and "Other NNs", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the efficacy of Google's Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon's fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks. [ABSTRACT FROM AUTHOR]

Online learning has many advantages, but it also has a drawback in that it is difficult for teachers to recognize the emotional state of students for adjusting the pace and attention to each learner. Based on a face dataset called FER-2013, we have classified seven basic types of human emotions from human face images using Deep Learning models with VGG16, ResNet, and MobileNetV2. We proposed a novel machine learning model called TTNet, a variant of the ResNet + VGGFace2 model that allows recognizing human emotions according to the above seven emotions from face images with more than 70% accuracy. Moreover, the software that implemented this machine learning model has been developed to automatically collect facial photo data and analyze the emotions of each learner in the online classroom to help teachers monitor learners' attitudes. Finally, we compared the recognition result of our software with those of six other methods and confirmed that TTNet outperforms the other methods on facial emotion recognition. Taken together, the TTNet deep learning model is a tool to effectively manage users' emotions in online conferences in general, effectively supporting the organization of online classes.

Emotion analysis from Bengali text data is challenging due to the intricate structure of the language itself and lack of resource availability tailored to Sentiment Classification. In this paper, the authors have used machine learning algorithms, particularly Gaussian Naive Bayes and Support Vector Machine, for the classification of six emotions in Bengali text. The data is comprehensively pre-processed through segmentation, emoticon handling, removal of stop words, and stemming. It uses feature selection techniques like unigram, bi-gram, and term frequency-inverse document frequency to improve classification accuracy. The main aim of the paper is to present an in-depth analysis of emotion detection in Bengali text, which would be very helpful to scholars working on NLP problems in non-English languages. This research, hence, fills up the gap in emotion analysis research for Bengali text, which has comparatively remained underexplored compared to other languages. The methodology involves dataset preparation, extensive preprocessing, feature extraction with selection, and classification. After rigorous experimentation, the accuracy attained with the GNB classifier is 93.83%, proving the effectiveness of the proposed model in capturing subtle emotional nuances in Bengali text.