Update README.md

README.md

@@ -16,30 +16,30 @@ metrics:
 ## Model Overview
 This model is a multi-class emotion classifier trained to identify nine distinct emotional states in text. The classes and their corresponding labels are as follows:
 
-- **Class 0**: Neutral
-- **Class 1**: Joy
-- **Class 2**: Surprise
+- **Class 0**: Anger
+- **Class 1**: Fear
+- **Class 2**: Disgust
 - **Class 3**: Sadness
-- **Class 4**: Anger
-- **Class 5**: Disgust
-- **Class 6**: Fear
-- **Class 7**: Trust
-- **Class 8**: Mixed emotions
+- **Class 4**: Joy
+- **Class 5**: Enthusiasm
+- **Class 6**: Hope
+- **Class 7**: Pride
+- **Class 8**: No emotion
 
 ### Dataset and Preprocessing
 The dataset combines original and synthetic data to improve class balance and performance. Below are the evaluation metrics for the model:
 
 | Class      | Precision | Recall | F1-Score | Support |
 |------------|-----------|--------|----------|---------|
-| Neutral (0)| 0.54      | 0.60   | 0.57     | 777     |
-| Joy (1)    | 0.85      | 0.76   | 0.80     | 776     |
-| Surprise (2)| 0.91     | 0.95   | 0.93     | 776     |
+| Anger (0)| 0.54      | 0.60   | 0.57     | 777     |
+| Fear (1)    | 0.85      | 0.76   | 0.80     | 776     |
+| Disgust (2)| 0.91     | 0.95   | 0.93     | 776     |
 | Sadness (3)| 0.87      | 0.84   | 0.86     | 775     |
-| Anger (4)  | 0.84      | 0.81   | 0.83     | 777     |
-| Disgust (5)| 0.64      | 0.62   | 0.63     | 776     |
-| Fear (6)   | 0.53      | 0.55   | 0.54     | 777     |
-| Trust (7)  | 0.75      | 0.81   | 0.78     | 776     |
-| Mixed emotions (8)| 0.67 | 0.65 | 0.66     | 1553    |
+| Joy (4)  | 0.84      | 0.81   | 0.83     | 777     |
+| Enthusiasm (5)| 0.64      | 0.62   | 0.63     | 776     |
+| Hope (6)   | 0.53      | 0.55   | 0.54     | 777     |
+| Pride (7)  | 0.75      | 0.81   | 0.78     | 776     |
+| No emotion (8)| 0.67 | 0.65 | 0.66     | 1553    |
 
 ### Overall Metrics
 - **Accuracy**: 0.72
@@ -47,7 +47,9 @@ The dataset combines original and synthetic data to improve class balance and pe
 - **Weighted Average**: Precision = 0.73, Recall = 0.72, F1-Score = 0.73
 
 ### Performance Insights
-The model achieves strong performance across most classes, particularly for "Surprise" and "Sadness." However, "Fear" and "Mixed emotions" exhibit lower recall and precision, suggesting potential areas for improvement. Future development could include targeted data augmentation or specialized techniques to handle these classes.
+The model achieves strong performance across most classes, particularly for "Sadness" and "Disgust." 
+However, "Enthusiasm" and "Hope" exhibit lower recall and precision, suggesting potential areas for improvement. 
+Future development could include targeted data augmentation or specialized techniques to handle these classes.
 
 ## Model Usage
 ### Applications
@@ -56,12 +58,13 @@ The model achieves strong performance across most classes, particularly for "Sur
 - Understanding emotional context for human-computer interaction.
 
 ### Limitations
-- Performance varies across classes, with some (e.g., "Fear" and "Mixed emotions") showing lower metrics.
+- Performance varies across classes, with some (e.g., "Enthusiasm" and "Hope") showing lower metrics.
 - The model may not generalize well to domains outside the training data.
 - Ambiguities in text can lead to misclassification, especially for overlapping emotional states.
 
 ### Ethical Considerations
-The model's predictions might not always align with human interpretations of emotions, particularly in ambiguous or context-dependent cases. Misclassification could lead to inappropriate conclusions if used in sensitive applications (e.g., mental health monitoring or automated decision-making).
+The model's predictions might not always align with human interpretations of emotions, particularly in ambiguous or context-dependent cases. 
+Misclassification could lead to inappropriate conclusions if used in sensitive applications (e.g., mental health monitoring or automated decision-making).
 
 ### Future Work
 - Improving performance on underrepresented classes using advanced augmentation or transfer learning techniques.