Enterprise AI Analysis
An enhanced social emotional recognition model using bidirectional gated recurrent unit and attention mechanism with advanced optimization algorithms
Authors: Taghreed Ali Alsudais et al. | Published: 21 November 2025
Social-emotional learning (SEL) is gradually becoming a region of attention for defining children's school readiness and forecasting academic success. It is the procedure of incorporating cognition, behaviour, and emotion into daily life. School structure contains systemic practices to integrate SEL into teaching and learning so that kids and adults construct social- and self-awareness, acquire the ability to handle their specific and other's feelings and behaviour, make reliable decisions, and build positive relations. Recent school-based programs have demonstrated that SEL greatly improves mental and physical health, academic success, moral judgment, citizenship, and motivation. This paper proposes a Deep Representation Model with Word Embedding and Optimization Algorithm for Social Emotional Recognition (DRMWE-OASER) methodology. The DRMWE-OASER methodology primarily aims to develop an effectual method for detecting social-emotional learning using advanced techniques. At first, the text pre-processing stage is applied at various levels to clean and convert text data into a meaningful and structured format. Moreover, the word embedding process is implemented using the TF-IDS method. Furthermore, a bidirectional gated recurrent unit with attention mechanism (BIGRU-AM) method is employed for classification. Finally, the improved whale optimizer algorithm (IWOA)-based hyperparameter selection process is utilized to optimize the classification results of the BIGRU-AM method. A wide range of experiments using the DRMWE-OASER approach is performed under emotion detection from text dataset. The experimental validation of the DRMWE-OASER approach portrayed a superior accuracy value of 99.50% over existing models.
Executive Summary: Pioneering AI in Social-Emotional Recognition
This paper introduces a groundbreaking Deep Representation Model with Word Embedding and Optimization Algorithm for Social Emotional Recognition (DRMWE-OASER), designed to revolutionize the detection of social-emotional learning from text. By integrating advanced pre-processing, innovative word embedding, and a highly optimized classification model, DRMWE-OASER achieves unparalleled accuracy and efficiency, setting a new standard for understanding and fostering emotional intelligence.
0
Overall Accuracy
0
Precision
0
Recall
0
F1-Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overall Approach
Text Processing
Word Embedding
BiGRU-AM Classification
Optimization
The DRMWE-OASER Framework
The DRMWE-OASER methodology is a novel deep representation model designed for highly accurate social-emotional recognition from text data. It integrates robust text pre-processing, advanced TF-IDF word embedding, a powerful BiGRU-AM classification model, and an improved Whale Optimization Algorithm (IWOA) for fine-tuning. This comprehensive framework ensures precise contextual understanding and optimal model performance, making it highly effective for complex linguistic analysis in SEL.
Advanced Text Pre-processing
Effective text pre-processing is crucial for preparing raw text data for machine learning. The DRMWE-OASER model employs a multi-stage process including: removing null values, keeping relevant columns (polarity, sentiment), eliminating duplicate entries, transforming sentiment values to numeric, tokenization using NLTK, eliminating stop words and special characters, and finally dividing data into training and testing sets. This ensures clean, structured, and meaningful input for subsequent stages.
TF-IDF Word Embedding
Word embedding transforms textual data into numerical vectors, capturing semantic relationships. The DRMWE-OASER model utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) method for this purpose. TF-IDF is chosen for its simplicity, interpretability, and efficiency. It quantifies the importance of a word in a document relative to a corpus, assigning higher weights to rare but informative terms. This approach generates robust input features that enhance the model’s ability to distinguish relevant patterns and semantic relationships without requiring extensive training data, making it suitable for domain-specific SEL datasets.
BiGRU-AM Classification
For classification, the DRMWE-OASER model employs a Bidirectional Gated Recurrent Unit with Attention Mechanism (BiGRU-AM). BiGRU effectively captures sequential dependencies by processing text in both forward and backward directions, overcoming limitations of traditional RNNs and unidirectional GRUs regarding gradient vanishing and long-term memory. The integrated Attention Mechanism further enhances performance by dynamically focusing on the most relevant parts of the sequence, allowing the model to weigh crucial input elements according to their significance in the task context. This combination balances efficiency, accuracy, and interpretability for precise social-emotional recognition.
IWOA-based Hyperparameter Optimization
To further enhance the classification results and model performance, the DRMWE-OASER employs an Improved Whale Optimization Algorithm (IWOA) for hyperparameter selection. IWOA is a metaheuristic optimization technique inspired by humpback whale foraging behavior. It features adaptive weights and reverse learning mechanisms to avoid local minima and accelerate convergence. This robust population-based strategy makes it highly effective for navigating noisy and high-dimensional search spaces, ensuring optimal parameter tuning for the BiGRU-AM model and contributing significantly to the overall accuracy and reliability of the DRMWE-OASER approach.
99.50%
Peak Accuracy Achieved by DRMWE-OASER
Enterprise Process Flow
Text Pre-processing
→
TF-IDS Word Embedding
→
BiGRU-AM Classification
→
IWOA Hyperparameter Optimization
→
Social Emotional Recognition Output
| Methodology | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---|---|---|---|---|
| XLNET | 98.23 | 91.49 | 75.66 | 75.25 |
| ROBERTa | 89.55 | 93.33 | 80.56 | 78.39 |
| DistilBERT | 90.54 | 95.09 | 77.99 | 73.36 |
| BILSTM | 89.59 | 93.62 | 77.27 | 81.11 |
| GRU Model | 96.03 | 89.07 | 77.53 | 80.01 |
| Lexicon | 99.16 | 89.31 | 71.47 | 80.24 |
| SVM (TF-IDF) | 94.42 | 95.18 | 73.00 | 81.49 |
| XLNet-BIGRU-Attention | 91.08 | 95.66 | 78.68 | 73.92 |
| CNN-BiLSTM | 90.09 | 94.12 | 77.82 | 81.82 |
| Bi-RNN | 96.79 | 89.65 | 78.28 | 80.73 |
| DRMWE-OASER | 99.50 | 95.93 | 83.18 | 84.61 |
Real-World Impact: Enhancing Educational Outcomes
The DRMWE-OASER model has significant implications for real-world applications, particularly in educational settings. By accurately recognizing social-emotional states from textual data, this technology can empower educators to tailor learning strategies, identify students requiring emotional support, and foster healthier classroom environments. Its high accuracy and efficiency make it ideal for integration into learning management systems, providing real-time insights that improve student well-being, academic performance, and overall social-emotional development. This proactive approach can lead to more resilient, empathetic, and successful individuals, bridging critical gaps in current SEL programs.
Calculate Your Potential AI ROI
Estimate the annual savings and efficiency gains your organization could achieve by implementing advanced AI solutions like DRMWE-OASER.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A typical phased approach to integrate advanced AI solutions into your enterprise.
Phase 01: Discovery & Strategy
Initial consultations to understand your specific needs, data infrastructure, and business objectives. We define project scope, success metrics, and a tailored AI strategy.
Phase 02: Data Integration & Pre-processing
Securely integrate with your existing data sources. Implement robust pre-processing pipelines, including advanced techniques like TF-IDF, to prepare your data for optimal model training.
Phase 03: Model Development & Training
Develop and train the custom BiGRU-AM model. Utilize IWOA-based optimization for hyperparameter tuning to ensure maximum accuracy and efficiency for your specific use case.
Phase 04: Validation & Refinement
Rigorous testing and validation of the model's performance against defined benchmarks. Iterative refinement based on feedback and real-world data simulations.
Phase 05: Deployment & Monitoring
Seamless integration of the AI solution into your operational environment. Establish continuous monitoring and performance analytics to ensure ongoing effectiveness and identify areas for future enhancement.
Ready to Transform Your Enterprise with AI?
Book a complimentary 30-minute consultation with our AI specialists to explore how DRMWE-OASER or other advanced AI solutions can unlock new efficiencies and insights for your business.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat