This repository contains Python code for performing sentiment analysis on the Twitter US Airline Sentiment dataset using the DistilBERT model. For Sentiment Analysis we use dataset file:'Tweets.csv',which can be found at Dataset Link: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment
The code provided in the repository demonstrates how to: Load and preprocess the dataset. Fine-tune a DistilBERT model for sentiment classification. Evaluate the model's performance using accuracy, a confusion matrix, and a classification report.
Python 3.x Libraries: transformers, torch, pandas, seaborn, matplotlib
Download the tweets dataset by using the provided link. Open the new notebook in colab. Upload the downloaded dataset to the session storage.
run 'AI_Phase5.ipynb' After Training is complete the program will evaluate its performance, and display the accuracy, confusion matrix, and classification report.