| #NLP-Sentiment-Analysis-Airline-Tweets-with-BERT-V2 | |
| 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 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 | | |
| 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 | |