Hybrid Deep Neural Network with Domain Knowledge for Text Sentiment Analysis
Bibliographic record
Abstract
Sentiment analysis (SA) analyzes online data to uncover insights for better decision-making. Conventional text SA techniques are effective and easy to understand but encounter difficulties when handling sparse data. Deep Neural Networks (DNNs) excel in handling data sparsity but face challenges with high-dimensional, noisy data. Incorporating rich domain semantic and sentiment knowledge is crucial for advancing sentiment analysis. To address these challenges, we propose an innovative hybrid sentiment analysis approach that combines established DNN models like RoBERTA and BiGRU with an attention mechanism, alongside traditional feature engineering and dimensionality reduction through PCA. This leverages the strengths of both techniques: DNNs handle complex semantics and dynamic features, while conventional methods shine in interpretability and efficient sentiment extraction. This complementary combination fosters a robust and accurate sentiment analysis model. Our model is evaluated on four widely used real-world benchmark text sentiment analysis datasets: MR, CR, IMDB, and SemEval 2013. The proposed hybrid model achieved impressive results on these datasets. These findings highlight the effectiveness of this approach for text sentiment analysis tasks, demonstrating its ability to improve sentiment analysis performance compared to previously proposed methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".