User Sentiment Analysis in Conversational Systems Based on Augmentation and Attention-based BiLSTM
Bibliographic record
Abstract
Conversational Systems are increasingly substituting humans in many service industries. They aim to provide human-like interaction with users for task completion or chitchat in a conversation style. User sentiment analysis is an important task that can help better understand users’ behavior and satisfaction in conversations. Although some researchers have studied the problem of sentiment analysis, most of the existing methods are oriented toward general felds. To overcome the challenges of sentiment analysis, we propose a BE-Att-BiLSTM, which stands for an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. The proposed model uses pre-trained BERT, contextual embeddings and a combination of BiLSTM and attention mechanism for efficient sentiment analysis in conversations. In addition, text-augmentation techniques are leveraged to enhance the performance of the proposed model. Experimental results on a public benchmark dataset show an improved accuracy of 68.00% and an F1-score of 67.50%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".