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README.md

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 #NLP-Sentiment-Analysis-Airline-Tweets-with-BERT-V2
 
-This repository showcases a sentiment analysis project utilizing BERT, a leading NLP model. The project involves preprocessing, tokenization, and fine-tuning BERT for sentiment classification of airline tweets.
-The tasks in this model use the original model "BERT base model (uncased) ", using datasets :https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment, It achieves the following results on the evaluation set
+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
   Accuracy: 0.8203551912568307
 
-  Colab Notebook for fine tuning:https://colab.research.google.com/drive/1IQen2iNXkjOgdzjyi7PQyLFqHyqHTF3A?usp=sharing
+  Colab notebook for improvements: https://colab.research.google.com/drive/1IQen2iNXkjOgdzjyi7PQyLFqHyqHTF3A?usp=sharing
 
+  #classification report for more detailed evaluation :
 
+|           | precision | recall | f1-score | support |
+|-----------|-----------|--------|----------|---------|
+| negative  |   0.88    |  0.90  |   0.89   |   959   |
+| neutral   |   0.68    |  0.58  |   0.62   |   293   |
+| positive  |   0.72    |  0.81  |   0.76   |   212   |
+|-----------|-----------|--------|----------|---------|
+| accuracy  |           |        |   0.82   |  1464   |
+| macro avg |   0.76    |  0.76  |   0.76   |  1464   |
+| weighted avg | 0.82  |  0.82  |   0.82   |  1464   |