Integrating VADER, deep learning, and nine Rasas for enhanced student emotion detection in social media text

Introduction

The rapid expansion of social media platforms has significantly transformed the way students express their opinions, experiences, and emotions related to academic environments. Platforms such as YouTube, Reddit, and Twitter generate vast amounts of unstructured textual data, offering valuable insights into student engagement, satisfaction, and learning experiences. Analyzing such data effectively has become essential for improving teaching strategies, curriculum design, and overall educational outcomes. Consequently, sentiment analysis has emerged as a powerful tool for extracting meaningful information from large-scale textual datasets (Zhang et al., 2022; Minaee et al., 2021) [26,36] . Traditional sentiment analysis approaches primarily focus on classifying text into broad polarity categories such as positive, negative, or neutral. While these methods are useful for general opinion mining, they fail to capture the complexity and subtlety of human emotions expressed in natural language, particularly in educational contexts where student feedback often contains nuanced and mixed emotional expressions (Khan et al., 2022) [18] . Lexiconbased approaches such as VADER provide fast and interpretable sentiment scoring but lack the ability to understand contextual relationships within text. On the other hand, deep learning models demonstrate strong performance in capturing contextual semantics but often suffer from limited interpretability and high computational complexity (Sarker, 2021; Basiri et al., 2023) [15,41] . Recent advancements in natural language processing have highlighted the effectiveness of hybrid approaches that combine lexicon-based and deep learning techniques. Such approaches aim to leverage the strengths of both paradigms interpretability from rule-based systems and contextual understanding from data-driven models. However, most existing studies still rely on coarse-grained sentiment outputs and do not incorporate frameworks capable of representing the full spectrum of human emotions (Dang et al., 2020) [38] . In this context, the Nine Rasas theory, derived from classical Indian aesthetics, provides a comprehensive and culturally rich framework for emotion representation. The Nine Rasas Shringara (love), Hasya (joy), Karuna (sadness), Raudra (anger), Veera (courage), Bhayanaka (fear), Bibhatsa (disgust), Adbhuta (surprise), and Shanta (peace) capture a wide range of human emotional states beyond simple sentiment polarity. Integrating this framework into sentiment analysis enables a more fine-grained and contextually meaningful understanding of student emotions (Acheampong et al., 2021) [39] . Despite the progress in sentiment analysis and emotion detection, several research gaps remain. Existing models either lack contextual understanding (in the case of lexiconbased approaches) or interpretability (in the case of deep learning models). Furthermore, limited work has been done on integrating culturally grounded emotion frameworks such as the Nine Rasas into hybrid sentiment analysis systems. Additionally, many studies rely on single-source datasets, which restricts the generalizability and robustness of the models.

To address these limitations, this study proposes a novel hybrid sentiment analysis framework that integrates the VADER lexicon-based model with deep neural networks and the Nine Rasas emotion model for fine-grained emotion detection. The proposed approach combines the efficiency and interpretability of VADER with the contextual learning capabilities of deep learning techniques, enabling improved sentiment classification performance. Furthermore, the integration of the Nine Rasas framework allows for a culturally enriched and multi-dimensional representation of student emotions. The proposed model is evaluated on a hybrid dataset that combines the GoEmotions benchmark dataset with real-world social media data collected from YouTube and Reddit. This multi-source dataset enhances the robustness and real-world applicability of the model. The performance of the proposed approach is assessed using standard evaluation metrics, including accuracy, precision, recall, and F1-score. The main contributions of this study are as follows:  A hybrid sentiment analysis framework integrating VADER and deep neural networks  Incorporation of the Nine Rasas model for fine-grained emotion classification  Utilization of a large-scale multi-source dataset combining benchmark and real-world data  Improved performance compared to standalone models in terms of accuracy and interpretability

Overall, this study aims to advance sentiment analysis in educational contexts by providing a robust, interpretable, and culturally enriched framework for student emotion detection.

Literature Review:

The field of sentiment analysis has witnessed significant advancements over the past decade, driven by the rapid growth of social media data and the need for extracting meaningful insights from unstructured text. This section presents a comprehensive review of existing techniques, focusing on lexicon-based methods, deep learning approaches, emotion detection, and the identified research gaps relevant to this study.

Sentiment Analysis Techniques

Sentiment analysis techniques can be broadly categorized into lexicon-based, machine learning, and deep learning approaches. Lexicon-based methods rely on predefined sentiment dictionaries to determine the polarity of text. One of the most widely used tools in this category is VADER, which is specifically designed for analyzing social media text and provides sentiment scores based on lexical features and rulebased heuristics (Hutto & Gilbert, 2021) [4] . These methods are computationally efficient and highly interpretable; however, they lack the ability to capture contextual nuances, sarcasm, and domain-specific variations in language (Sarker, 2021) [15] . Machine learning approaches, including Support Vector Machines (SVM), Naïve Bayes, and Decision Trees, have been widely applied to sentiment classification tasks. These methods improve classification accuracy compared to lexicon-based techniques by learning patterns from labeled data. However, they rely heavily on feature engineering techniques such as TF-IDF and n-grams, which limits their scalability and adaptability across different domains (Khan et al., 2022) [18] . [26] . These models effectively capture semantic relationships and long-range dependencies within textual data. Despite their advantages, deep learning models are often associated with certain limitations, including high computational complexity and reduced interpretability. Moreover, most deep learning-based sentiment analysis systems focus on coarse-grained polarity classification and do not provide detailed insights into specific emotional states (Minaee et al., 2021; Dang et al., 2020) [36,38] .

Deep

Emotion Detection from Text:

Emotion detection extends traditional sentiment analysis by identifying specific emotional states expressed in text. Existing approaches typically classify emotions into categories such as joy, anger, sadness, and fear. While these methods provide more detailed insights than polarity-based classification, they are often limited to a small set of predefined emotions and lack cultural and contextual depth (Acheampong et al., 2021) [39] .

Recent studies have explored the use of benchmark datasets such as GoEmotions for fine-grained emotion classification. However, these approaches still lack integration with culturally grounded emotion frameworks, which can provide a more comprehensive representation of human emotional experiences.

Social Media-Based Student Feedback Analysis

Social media platforms have emerged as valuable sources of student feedback, enabling real-time analysis of opinions, experiences, and emotional responses. Several studies have applied sentiment analysis techniques to educational data to assess student satisfaction, engagement, and academic performance (Rane & Kumar, 2023) [40] . These studies demonstrate the potential of sentiment analysis in improving decision-making processes in educational institutions. However, most existing systems rely on basic sentiment polarity and fail to capture the nuanced emotional expressions present in student-generated content. This limitation highlights the need for more advanced models capable of fine-grained emotion detection in educational contexts.

Research Gap

Despite significant advancements in sentiment analysis, several critical limitations remain. Lexicon-based methods such as VADER lack contextual understanding and fail to capture complex linguistic patterns. Machine learning models improve performance but require extensive feature engineering and struggle with generalization across domains. Deep learning approaches, while highly effective in capturing contextual semantics, often lack interpretability and remain focused on coarse-grained sentiment classification. Furthermore, existing emotion detection frameworks are typically limited to a small set of basic emotions and do not incorporate culturally rich models such as the Nine Rasas.

The absence of such frameworks restricts the ability to capture the full spectrum of human emotional expression, particularly in culturally diverse contexts.

Additionally, most studies rely on single-source datasets, which limits the robustness and real-world applicability of sentiment analysis models. There is a clear lack of hybrid approaches that integrate lexicon-based methods, deep learning techniques, and culturally grounded emotion models within a unified framework.

To address these gaps, this study proposes a hybrid sentiment analysis framework that combines VADER, deep neural networks, and the Nine Rasas model to achieve fine-grained and culturally enriched emotion detection from multi-source student-generated data. The literature indicates that while significant progress has been made in sentiment analysis, existing approaches are limited in capturing the full spectrum of human emotions, particularly in educational contexts. This motivates the development of a hybrid framework that integrates lexiconbased methods, deep learning, and the Nine Rasas model to achieve more accurate and culturally enriched emotion detection.

Proposed Methodology

This study proposes a hybrid sentiment analysis framework that integrates lexicon-based sentiment scoring, deep learning-based contextual feature extraction, and culturally grounded emotion mapping using the Nine Rasas model. The objective of the proposed methodology is to achieve accurate and fine-grained emotion detection from student-generated social media text. By combining the interpretability of rulebased systems with the contextual learning capabilities of deep neural networks, the proposed framework addresses the limitations of traditional sentiment analysis approaches (Sarker, 2021; Basiri et al., 2023) [15,41] . The overall architecture consists of multiple stages, including data collection, preprocessing, sentiment scoring using VADER, deep learning-based feature extraction, feature fusion, and emotion mapping. This integrated approach enables both polarity detection and fine-grained emotional classification.

Fig 1: System Pipeline

Figure 1 illustrates the overall system pipeline of the proposed hybrid framework. The process begins with data collection from multiple social media sources, followed by preprocessing. The cleaned data is then processed in parallel through the VADER sentiment module and the deep learning model. The outputs are fused and mapped to the Nine Rasas emotional categories to generate the final prediction.

Data Collection:

The dataset used in this study is collected from multiple sources, including YouTube, Reddit, and the GoEmotions benchmark dataset. These platforms provide diverse and real-world textual data reflecting student opinions, experiences, and emotional expressions. The use of a multi-source dataset enhances the robustness and generalizability of the proposed model (Rane & Kumar, 2023) [40] .

Data

Where 𝑃(𝐷) represents the preprocessing function.

The preprocessing pipeline includes removal of URLs, special characters, and stopwords, followed by tokenization, normalization, and lemmatization. These steps ensure consistency and improve the quality of input data for both lexicon-based and deep learning models (Minaee et al., 2021) [36] .

VADER Sentiment Module

The VADER (Valence Aware Dictionary and sEntiment Reasoner) model is employed to compute sentiment polarity scores for each text sample. VADER is a rule-based model specifically designed for social media text and provides four sentiment metrics: positive, negative, neutral, and compound scores (Hutto & Gilbert, 2021) [4] .

The compound sentiment score is computed as [36,41] .

Feature Fusion Mechanism

To combine the strengths of both approaches, the outputs from VADER and the deep learning model are fused. [38] .

Nine Rasas Emotion Mapping

The final stage maps the fused sentiment output to the Nine Rasas emotional categories. This mapping provides a fine-grained emotional interpretation beyond traditional sentiment classes. The proposed methodology introduces a hybrid framework that integrates lexicon-based sentiment analysis, deep learning, and a culturally enriched emotion model. This approach not only improves classification accuracy but also enables deeper emotional understanding of student feedback, making it highly suitable for educational analytics applications (Dang et al., 2020) [38] .

System Architecture

The system architecture of the proposed hybrid sentiment analysis framework is designed to efficiently process largescale social media data and generate fine-grained emotional insights using the integration of lexicon-based, deep learning, and Nine Rasas-based classification. The architecture consists of multiple interconnected modules, each responsible for a specific task in the sentiment analysis pipeline.

Architectural Overview

The proposed architecture follows a modular pipeline structure, where each component contributes to transforming raw textual data into meaningful emotional categories. The system integrates the rule-based VADER model with a deep learning-based classifier and a cultural emotion mapping layer.

System Architecture Diagram

The system architecture consists of five major components: Data Collection, Preprocessing, VADER Sentiment Analysis, Deep Learning-based Emotion Classification, and Fusion Layer for final prediction.

. Data Acquisition Layer

This layer is responsible for collecting raw textual data from various social media platforms such as YouTube, Reddit, and Twitter. The collected data consists of student-generated comments, feedback, and discussions, which serve as the primary input to the system (Rane & Kumar, 2023) [40] .

Data Preprocessing Layer

The preprocessing module cleans and standardizes the input text by removing noise such as URLs, special characters, and stopwords. It also performs tokenization and normalization to prepare the data for further analysis. This step significantly improves model performance and accuracy (Sarker, 2021) [15] .

Feature Extraction Layer

In this stage, textual data is converted into numerical representations suitable for machine learning and deep learning models. Techniques such as word embeddings or vectorization are used to capture semantic relationships within the text (Minaee et al., 2021) [36] .

Sentiment Analysis Layer

This layer consists of two parallel components:

a) Lexicon-Based Analysis

The VADER model computes sentiment polarity scores based on predefined lexical rules. It provides fast and interpretable sentiment outputs.

b) Deep Learning Analysis

A neural network-based model processes the input text to capture contextual and semantic information. This component enhances the system's ability to understand complex linguistic patterns and implicit sentiments (Basiri et al., 2023) [41] .

Feature Fusion Layer

The outputs from the VADER model and the deep learning model are combined in this layer. The fusion mechanism integrates polarity scores with contextual features to produce a more robust and accurate sentiment representation.

Emotion Mapping Layer (Nine Rasas)

This layer transforms the fused sentiment output into one of the Nine Rasas emotional categories. Unlike traditional sentiment classification, this step provides fine-grained emotional interpretation, enabling deeper analysis of student feedback (Acheampong et al., 2021) [39] .

Output Layer

The final output includes:  Sentiment polarity (Positive/Negative/Neutral)  Emotion label (Nine Rasas)

This dual-output system enhances interpretability and practical usability in educational analytics.

The proposed system architecture effectively integrates multiple analytical layers to overcome the limitations of traditional sentiment analysis methods. By combining the strengths of lexicon-based models, deep learning techniques, and the Nine Rasas framework, the system delivers a comprehensive and culturally enriched approach to student emotion detection (Dang et al., 2020) [38] .

Experimental Setup:

This section describes the experimental configuration used to evaluate the performance of the proposed hybrid sentiment analysis framework. It includes details about the dataset, tools and technologies, preprocessing techniques, model configuration, and evaluation metrics. The dataset was divided into training (70%), validation (15%), and testing (15%) sets.

Dataset Description:

The dataset used in this study is a hybrid corpus constructed from both benchmark and real-world sources. The GoEmotions dataset contributes 150,000 labeled samples covering 27 fine-grained emotions. Additionally, 80,000 comments were collected from YouTube and 50,000 from Reddit, focusing on student-related discussions, academic stress, and learning experiences. The combined dataset consists of 280,000 samples. Social media data were preprocessed and automatically labeled using VADER sentiment analysis followed by rule-based mapping to the Nine Rasas emotional categories.

Tools and Technologies:

The preprocessing steps include removal of URLs, special characters, stopwords, tokenization, lowercasing, and duplicate elimination to ensure data quality.

The implementation of the proposed system is carried out using the following tools and libraries: These tools enable efficient processing, training, and evaluation of the proposed hybrid model (Sarker, 2021) [15] .

Data Preprocessing Setup

The preprocessing pipeline includes the following steps:

 Lowercasing text  Removal of punctuation, URLs, and special characters

This step ensures that the input data is clean and consistent, improving the performance of both lexicon-based and deep learning models (Minaee et al., 2021) [36] .

Model Configuration

Evaluation Metrics

To evaluate the performance of the proposed model, standard classification metrics are used: [18] .

Baseline Models for Comparison

To validate the effectiveness of the proposed hybrid model, the following baseline models are used: The experimental setup is designed to ensure a fair and comprehensive evaluation of the proposed hybrid sentiment analysis framework. By combining multiple data sources, robust preprocessing techniques, and standard evaluation metrics, the study aims to demonstrate the effectiveness and reliability of the proposed approach in real-world educational scenarios (Basiri et al., 2023) [41] .

Results and Discussion

This section presents the performance evaluation of the proposed hybrid sentiment analysis model and compares it with baseline approaches. The results demonstrate the effectiveness of integrating VADER with a deep learning model and Nine Rasas-based emotional mapping. Training and validation performance were monitored across multiple epochs to ensure model convergence and avoid overfitting.

Performance Evaluation

The proposed model was evaluated using standard classification metrics such as Accuracy, Precision, Recall, and F1-Score. The results indicate that the hybrid approach outperforms individual models in capturing both sentiment polarity and fine-grained emotional nuances. [41] .

Confusion Matrix Analysis

The confusion matrix provides insights into classification performance across different sentiment classes. The confusion matrix demonstrates strong diagonal dominance, indicating that the proposed hybrid model achieves high classification accuracy across all sentiment categories. The model shows minor confusion between neutral and negative classes, which is expected due to overlapping semantic expressions in student feedback data collected from YouTube and Reddit platforms.

Nine Rasas Emotion Analysis

The integration of Nine Rasas provides deeper emotional insights beyond traditional sentiment classification. This demonstrates that the proposed model captures culturally rich emotional diversity, which is not possible with traditional sentiment analysis methods (Rane & Kumar, 2023) [40] . [38] . The performance of the proposed hybrid sentiment analysis framework was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The results were compared with standalone approaches, including the VADER model and the deep learning model.

Comparative Analysis

The proposed hybrid model achieved an overall accuracy of 91%, outperforming the VADER model (78%) and the standalone deep learning model (86%). This improvement demonstrates the effectiveness of combining lexicon-based and deep learning approaches for sentiment analysis. The superior performance of the hybrid model can be attributed to the complementary strengths of its components.

The VADER model provides interpretable sentiment scores and effectively handles explicit sentiment expressions, while the deep learning model captures contextual and semantic relationships within the text. The feature fusion mechanism enables the model to leverage both rule-based and contextual information, resulting in improved classification accuracy. Furthermore, the integration of the Nine Rasas framework enhances the model's ability to perform fine-grained emotion classification. Unlike traditional sentiment analysis approaches that focus on polarity, the proposed model captures a broader spectrum of emotional states, making it more suitable for student-generated content. However, certain limitations were observed. The model exhibited minor misclassification in cases involving mixed or ambiguous emotions, particularly when multiple emotional cues were present within the same text. Additionally, deep learning models require substantial computational resources, which may limit real-time deployment in resource-constrained environments.

Overall, the results indicate that the proposed hybrid framework provides a robust and effective solution for sentiment analysis, offering both improved accuracy and deeper emotional insights compared to existing approaches.

Conclusion & Future Work

This study proposed a hybrid sentiment analysis framework that integrates the VADER lexicon-based model, deep neural networks, and the Nine Rasas emotion model for fine-grained emotion detection. The proposed approach addresses the limitations of traditional sentiment analysis methods by combining interpretability with contextual understanding.

The experimental results demonstrate that the hybrid model outperforms standalone approaches, achieving an accuracy of 91% along with improved precision, recall, and F1-score. The integration of the Nine Rasas framework further enhances the model's ability to capture diverse emotional states, providing a more comprehensive understanding of student emotions.

The proposed framework has significant implications for educational analytics, enabling better monitoring of student engagement and emotional well-being. It can also be extended to other domains such as social media monitoring, customer feedback analysis, and mental health assessment. Future work may focus on incorporating transformer-based models such as BERT to further improve contextual understanding. Additionally, expanding the dataset and exploring multilingual sentiment analysis can enhance the scalability and applicability of the proposed approach.

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