A Hybrid Attention-Driven Recurrent Neural Network Model for Sentiment Classification of Social Media Texts
Bibliographic record
Abstract
With the rapid expansion of user-generated content on social media platforms like Twitter, Facebook, and Reddit, accurately identifying sentiment from textual data has become an essential yet challenging task due to the informal, noisy, and contextually diverse nature of these platforms. To address this, we propose a Hybrid Attention-Driven Recurrent Neural Network (HA-RNN) model that effectively combines Bidirectional Gated Recurrent Units (Bi-GRU) with a sophisticated attention mechanism for sentiment classification. The model utilizes pre-trained GloVe embeddings (300 dimensions) to capture rich semantic features from raw text, enhancing the initial representation of social media data. The Bi-GRU layers are employed to model sequential dependencies Safeyah Tawil Department of Computer Science and Engineering, Faculty of Information Technology, Zarqa University, Zarqa, Jordan. University of Business and Technology, Jeddah, Saudi Arabia stawil@zu.edu.jo I. INTRODUCTION Social media platforms have become primary channels for individuals to express opinions, emotions, and sentiments on a wide range of topics, including politics, products, services, and global events. The explosive growth of platforms such as Twitter, Facebook, and Instagram has led to an overwhelming amount of unstructured textual data that offers valuable insights into public sentiment [1]. Analyzing this vast content can support businesses, governments, and researchers in understanding user perceptions, improving services, and detecting social trends. in both forward and backward directions, ensuring a comprehensive understanding of context within a sentence. The integrated attention layer enables the model to dynamically focus on sentiment-bearing words, thereby improving classification accuracy and interpretability. We evaluated the proposed model on two widely recognized datasets: the Twitter US Airline Sentiment Dataset and the Sentiment140 Dataset. The HA-RNN achieved an accuracy of 90.8% on the Twitter US Airline dataset and 88.5% on Sentiment140, outperforming traditional models such as CNN (84.3% accuracy), LSTM (86.7%), and Bi-GRU without attention (87.1%). Furthermore, the attention mechanism provided insightful visualization, highlighting the critical words influencing sentiment predictions. The model demonstrated a balanced performance with high precision, recall, and F1-scores, validating its robustness across different sentiment classes. Overall, the HA- RNN model presents an effective and interpretable solution for sentiment analysis on noisy and diverse social media texts, supporting applications in social monitoring, brand analysis, and opinion mining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".