Analytics: Retention Strength
RNN Gradient Flow
LSTM Gradient Flow
Word-to-Vector Pipeline
Waiting for input...
Final Prediction
Negative
0.00
Positive
Word Influence Heatmap
Click any word to see its contribution
📝 Key Insight: Word order matters in sentiment analysis. The word "not"
before "good" flips the sentiment entirely. LSTMs handle this through their gating
mechanism, while RNNs struggle to maintain context over longer distances.
RNN
Gradient Magnitude
Confusion Matrix
LSTM
Gradient Magnitude
Confusion Matrix
📝 Key Insight: The RNN's gradient bars shrink over epochs (vanishing
gradient), causing its validation accuracy to plateau around 79%. The LSTM maintains stable
gradients through its cell state highway, converging smoothly to ~87% validation accuracy.
Word Attribution — Sentence A
LSTM Cell State Magnitude
Compare Mode — Sentence B
LSTM Cell State Magnitude — Sentence B
📝 Key Insight: RNNs assign strong influence to recent sentiment words but
weak influence to early context. LSTMs distribute influence more evenly, allowing context
words to shape the cell state over time. Compare sentences A and B to see how word order
changes attribution.
📊 Accuracy Comparison
| Metric | RNN | LSTM |
|---|---|---|
| Train Accuracy | 0% | 0% |
| Val Accuracy | 0% | 0% |
| Test Accuracy | 0% | 0% |
📈 Accuracy Bar Chart
📉 ROC Curve
📉 Precision-Recall Curve
🎯 F1 Score Comparison
🔑 Key Takeaways:
- LSTM handles long-term dependencies significantly better than RNN.
- RNN suffers from vanishing gradient, limiting its learning capacity.
- LSTM generalizes better — higher validation and test performance.
- Despite using only 64 units vs RNN's 256, LSTM outperforms across all metrics.