Sentiment Analysis of US Airline Tweets
Goal: The goal of this project is to classify tweets related to US airlines into positive, neutral, or negative sentiments. The students will design and implement a classification model to predict the sentiment of airline-related tweets. They will experiment with various machine learning or deep learning models, then evaluate their performance based on accuracy, precision, recall, and F1- score. Students are encouraged to explore model optimization and fine-tuning techniques using frameworks like Scikit-learn, TensorFlow, Keras, Transformer or PyTorch. Dataset: The dataset is the US Airline Sentiment Dataset, containing 14,640 tweets labeled as positive, neutral, or negative. The task is to predict the sentiment of each tweet, making it a multiclass classification problem.