Emotion Classification Research Articles

Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering

This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and sad books is suggested for happy users to acknowledge the nuanced interplay of emotions. Neutral-themed books are proposed to users with a neutral mood for balance. The system employs an emotion classification algorithm for accurate mood detection, analyzing book summaries to extract emotional tones. Users' emotional states are evaluated using a Likert scale, adjusting recommendations accordingly. The methodology involves data collection, preprocessing, emotion classification, and mood meter operation. Results demonstrate the system's effectiveness in providing tailored book recommendations, enriching users' reading experiences.

Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.

Hcnnxgboost: A Hybrid Cnn-Xgboost Approach for Effective Emotion Detection in Textual Data

in recent years, emotional analysis has become a key focus in computational studies, driven by the need to understand societal sentiments. this exploration is motivated by its many promising applications, such as community well-being evaluation, human-computer interaction, suicide prevention, and personalized recommendations. even with progress in other areas like identifying expressions from facial cues and speech, the study of text-based emotion recognition remains a fascinating field of research because machines struggle to interpret context, especially compared to human capabilities. our research addresses this by introducing a multiclass text-based emotion detection system that combines a cnn architecture with xgboost for improved classification in natural language processing. we pre-process publicly available datasets and use glove pre-trained word embeddings for better text representation. a major contribution of our work is enhancing the feature space by combining cnn probabilities with the original text data. the proposed hcnnxgboost model outperforms all other machine learning and deep learning algorithms across the emoint, isear, and crowdflower datasets, achieving f-scores of 90.1%, 87.4%, and 62.2%, respectively. experimental evaluations on benchmark datasets show better f-scores, confirming the effectiveness of our approach. comparisons with other classifiers highlight the enhanced performance and effectiveness of our hybrid cnnxgboost (hcnnxgboost) model, making it one of the best solutions for emotion classification in natural language processing tasks.

Towards Emotionally Intelligent Virtual Environments: Classifying Emotions through a Biosignal-Based Approach

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.

Evaluation of emotion classification schemes in social media text: an annotation-based approach

BackgroundEmotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis?MethodsWe proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval’s four emotions, Ekman’s six basic emotions, ancient China’s Seven Emotions, Plutchik’s eight primary emotions, and GoEmotions’ 27 emotions.ResultsThe AHP evaluation result shows that Ekman’s scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman’s six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions’ joy aligns with Ekman’s happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman’s corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman’s, such as approval, love, pride, amusement, etc.ConclusionEkman’s scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

Latent profile analysis of transdiagnostic emotional distress, suicidality, and resilience in first responders

BackgroundFirst responders (i.e., law enforcement officers, firefighters, and emergency medical technicians/paramedics), experience significantly higher occupational trauma exposure than U.S. adult workers outside these fields, leading to increased risks of comorbid mental health disorders. Repeated and intense trauma exposure may combine with personal factors to place them at higher risk for suicide. Conversely, first responders may show higher levels of psychological resilience in the face of occupational trauma experiences. Some research exists on resilience, though little is known about suicide resilience in first responder populations. MethodsWe used latent profile analysis (LPA) on a treatment-seeking sample of first responders (N = 340) with measures of posttraumatic stress disorder (PTSD), generalized anxiety, depression, suicidality, and resilience. ResultsWe determined the best fit was a five-class solution, including the following emotional distress categories: minimal (19 %), mild (33 %), moderate (8 %), moderately severe (27 %), and severe (13 %) emotional distress. In this study, all multivariate analyses of variance (MANOVAs) were statistically significant and had large effect sizes ranging from the lowest (resilience) to the largest (depression). LimitationsWe used self-report assessments and not a clinical interview. Also, we did not have data on measures of substance use, emotional dysregulation (e.g., attachment), or trauma exposure. ConclusionsThis study underscores the critical need for developing and implementing transdiagnostic interventions that not only address the spectrum of emotional distress and suicidality but also actively enhance resilience among treatment-seeking first responders.

A method of developing ensemble classifiers for recognizing audio data of various nature

The work developed a method of building ensemble classifiers for recognizing audio data of various nature. The method is a tentative date and requires the following steps. In the first step, the input datasets are selected, which are transformed and divided into training and test samples, respectively. RAVDESS datasets are chosen for the task of audio emotion recognition, music genre recognition is performed on the GTZAN dataset. In the second step, the following seven classifiers were created and investigated as elements of ensemble classifiers: K Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, Multilayer Perceptron, Convolutional Neural Network and Long Short-term Memory. In the process of training elementary classifiers, the corresponding hyperparameters were adjusted using the Grid Search approach. In the third step, elementary classifiers were combined using the stacking ensemble method with such types of aggregation as soft voting, hard voting, soft voting using the GOMPERTZ function. All possible ensemble combinations starting with three elementary classifiers for recognizing audio emotions and music genres were tested. Therefore, the total number of ensembles studied in the work was 297. The research results for the problem of audio emotion classification showed that the accuracy of recognition according to the Accuracy metric of the best ensemble classifier is 8.1% higher than that of the best elementary classifier in its composition, which is based on on MLP, and according to the F1 metric, this indicator is 8% higher. For the task of recognizing music genres, the corresponding indicators are higher by 5.6 % and 5.2 %, respectively.

What Is Faster than Where in Vocal Emotional Perception.

Voices carry a vast amount of information about speakers (e.g., emotional state; spatial location). Neuroimaging studies postulate that spatial ("where") and emotional ("what") cues are processed by partially independent processing streams. Although behavioral evidence reveals interactions between emotion and space, the temporal dynamics of these processes in the brain and its modulation by attention remain unknown. We investigated whether and how spatial and emotional features interact during voice processing as a function of attention focus. Spatialized nonverbal vocalizations differing in valence (neutral, amusement, anger) were presented at different locations around the head, whereas listeners discriminated either the spatial location or emotional quality of the voice. Neural activity was measured with ERPs of the EEG. Affective ratings were collected at the end of the EEG session. Emotional vocalizations elicited decreased N1 but increased P2 and late positive potential amplitudes. Interactions of space and emotion occurred at the salience detection stage: neutral vocalizations presented at right (vs. left) locations elicited increased P2 amplitudes, but no such differences were observed for emotional vocalizations. When task instructions involved emotion categorization, the P2 was increased for vocalizations presented at front (vs. back) locations. Behaviorally, only valence and arousal ratings showed emotion-space interactions. These findings suggest that emotional representations are activated earlier than spatial representations in voice processing. The perceptual prioritization of emotional cues occurred irrespective of task instructions but was not paralleled by an augmented stimulus representation in space. These findings support the differential responding to emotional information by auditory processing pathways.

Crafting for Emotion Appropriateness in Affective Robotics: Examining the Practicality of the OCC Model

Research in affective robotics has been using emotions to improve human-robot interaction. One important aspect has been to design recognizable and believable emotions in robotics. Recent work argued that externally displayed emotions on robots may or may not be appropriate for a given situation. However, the selection of emotions as appropriate/inappropriate is not trivial. We here examine the practicality of an established model to craft for emotion appropriateness based on situations of interaction. To do so, we explored the use of the Ortony, Clore, and Collins (OCC) model, which provides a psychological framework of appraisal in which the characteristics of situations are defined and connected to emotions, to identify emotion categories and create contrasting perceptions of emotion appropriateness. We then mapped these categories to four recognizable emotions on aerial robots and designed two video clips (3min35s each) of respectively appropriate and inappropriate emotions. The clips were evaluated in an online study ( N =100) where significant differences were found in attitudes toward the robot's emotions. This paper contributes initial findings to designing for emotion appropriateness.

Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.

Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain-computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.

A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning.

Artificial intelligence and human-computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states and speech. However, endowing skin-like interfaces with such intricate perception abilities remains challenging. Here, we have developed a flexible piezoresistive artificial ear (AE) sensor based on gold nanoparticles, which can convert sound signals into electrical signals through changes in resistance. By testing the sensor's performance at both frequency and sound pressure level (SPL), the AE has a frequency response range of 20 Hz to 12 kHz and can sense sound signals from up to 5 m away at a frequency of 1 kHz and an SPL of 126 dB. Furthermore, through deep learning, the device achieves up to 96.9% and 95.0% accuracy in classification and recognition applications for seven emotional and eight urban environmental noises, respectively. Hence, on one hand, our device can monitor the patient's emotional state by their speech, such as sudden yelling and screaming, which can help healthcare workers understand patients' condition in time. On the other hand, the device could also be used for real-time monitoring of noise levels in aircraft, ships, factories, and other high-decibel equipment and environments.

Mapping the Online Reviews Sentiment Landscape: An Exploration of Emotion Spectrum in User Reviews of Mobile Apps

Mobile applications have emerged as powerful platforms for consumers and understanding the attitudes and reactions of users by content and emotional sides in a mobile context becomes crucial for marketing decision-making. The study with comprehensive approach aims to analyze the emotion spectrum in mobile applications user reviews context by examining specific emotions (anger, disgust, fear, joy, neutral, sadness, surprise). Sentiment analysis methodology (through "Emotion English DistilRoBERTa-base" transformers-model) is employed on the dataset of 302.647 reviews from 34 mobile application categories. Categorical examination of emotions indicates that neutrality is the dominant emotion category, followed by joy, sadness, disgust, surprise, and anger emotion categories, while the fear category is the least dominant category. According to polarity examination; negative polarity reviews are associated with neutral, sadness and disgust emotions; neutral polarity reviews are associated with neutral and sadness; positive polarity reviews are associated with neutral and joy emotion categories. Final part of analysis includes examination of emotions individually and mobile app categories which each emotion with the highest frequency of dominance are presented. The distribution rates of emotions and the individual relationships of emotions with different application categories can shed light on future academic research and marketing decision-making.

Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.

AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for audio, video and textual emotion recognition (i.e., AVaTER) that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model's ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

Commonalities and variations in emotion representation across modalities and brain regions

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains.

CENN: Capsule-enhanced neural network with innovative metrics for robust speech emotion recognition

Speech emotion recognition (SER) plays a pivotal role in enhancing Human-computer interaction (HCI) systems. This paper introduces a groundbreaking Capsule-enhanced neural network (CENN) that significantly advances the state of SER through a robust and reproducible deep learning framework. The CENN architecture seamlessly integrates advanced components, including Multi-head attention (MHA), residual module, and capsule module, which collectively enhance the model's capacity to capture both global and local features essential for precise emotion classification. A key contribution of this work is the development of a comprehensive reproducibility framework, featuring novel metrics: General learning reproducibility (GLR) and Correct learning reproducibility (CLR). These metrics, alongside their fractional and perfect variants, offer a multi-dimensional evaluation of the model's consistency and correctness across multiple executions, thereby ensuring the reliability and credibility of the results. To tackle the persistent challenge of overfitting in deep learning models, we propose an innovative overfitting metric that considers the intricate relationship between training and testing errors, model complexity, and data complexity. This metric, in conjunction with the newly introduced generalization and robustness metrics, provides a holistic assessment of the model's performance, guiding the application of regularization techniques to maintain generalizability and resilience. Extensive experiments conducted on benchmark SER datasets demonstrate that the CENN model not only surpasses existing approaches in terms of accuracy but also sets a new benchmark in reproducibility. This work establishes a new paradigm for deep learning model development in SER, underscoring the vital importance of reproducibility and offering a rigorous framework for future research.

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

Grief is the price we pay for love: Emotions disclosed by Chinese mourners in online memorial platform

The present paper undertakes an in-depth analysis of the messages within the ‘Grief-expressing’ section of the online memorial platform ‘Heavenly Remembrance.’ Utilizing Grounded Theory alongside a mixed-methods approach incorporating both qualitative and quantitative methodologies, this study delves into the emotional categories and referential features in the online discourse of Chinese mourners, drawing upon the Affective Lexicon Ontology. The findings of this study reveal that: firstly, the expressions of Chinese online mourners demonstrate a remarkable diversity and complexity, encompassing a spectrum of emotional categories characterized by both positive and negative attributes. This highlights the capacity of online memorial platforms to provide inclusive spaces for emotional disclosure, thereby facilitating the transformation of traditionally singular and concentrated grief into more personalized and varied emotional experiences. Secondly, the emotional disclosure of Chinese online mourners is directed towards the self, the deceased and the community respectively. Notably, there appears to be a trend over time towards a gradual transition in emotional expression from predominantly negative to increasingly positive, as well as from predominantly individual-focused to more collective-oriented expressions. In light of this, the study also attempts to explain the motivations behind such grief disclosure through the Motivation Model of Pragmatics, thereby theoretically contributing to the research on discourse and emotion in the Chinese mourning context and offering a new perspective on the emotional dynamics of individuals experiencing bereavement in online context.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques

EEG-based emotion Classification is used to distinguish and observe the mental state or emotions. Emotion classification using EEG is used for medical, security, and various other purposes. There are several deep learning and machine learning techniques that are used to classify EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and a high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN)and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here the input EEG signals are taken from two benchmark datasets namely as DEAP and K-EmoCon datasets. Then the input EEG signals are pre-processed and the features are extracted using ContextNet with Global principal component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) method. Then the weight parameters of the RSTAGNN are optimized using Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The performance of the proposed method using DEAP dataset attains higher accuracy by 24.05%, 12.64%relatedto existing systems, like DWT-SVM-EEG-EA-DEAP and GCNN-LSTM-EEG-EA-DEAP respectively and the performance of the proposed system using K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like SigRep-EEG-EA-K- EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques on Deap and K- EmoCon Article and CAT-EEG-EA-K-EmoCon respectively.