Update README.md

README.md

@@ -3,7 +3,8 @@
 This repository features sentiment analysis projects that leverage BERT, a leading NLP model.
 This project involves pre-processing, tokenization, and BERT customization for airline tweet sentiment classification.
 The tasks in this model use the original model "BERT base model (no casing)",
-uses a data set: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment, and there are several stages in achieving results, below are the evaluation sets
+uses a data set: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment, 
+and there are several stages in achieving results, below are the evaluation sets
   Accuracy: 0.8203551912568307
 
   Colab notebook for improvements: https://colab.research.google.com/drive/1IQen2iNXkjOgdzjyi7PQyLFqHyqHTF3A?usp=sharing
@@ -20,7 +21,23 @@ uses a data set: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sen
 | macro avg |   0.76    |  0.76  |   0.76   |  1464   |
 | weighted avg | 0.82  |  0.82  |   0.82   |  1464   |
 
-
+The sentiment classification model achieved a promising
+overall accuracy of 82.04%, built on BertForSequenceClassifi-
+cation and trained for 10 epochs using AdamW optimization.
+The model exhibited stable performance, with validation ac-
+curacy consistently between 0.79 to 0.81, indicating effective
+learning. Additionally, it showed high precision, particularly
+for negative sentiment (0.88), along with moderate scores for
+neutral (0.68) and positive (0.72) sentiments. These results
+were supported by recall and F1-score metrics, providing a
+comprehensive understanding of performance across sentiment
+classes. The analysis of the confusion matrix revealed strong
+alignment between model predictions and actual labels, al-
+beit with opportunities for improvement, such as addressing
+overfitting or parameter adjustment, evident from performance
+fluctuations across epochs.
+
+Developed by:Mastika