Twitter US Airline Sentiment

The Twitter US Airline Sentiment dataset is a collection of tweets aimed at analyzing public sentiment toward major U.S. airlines. Compiled in February 2015, the dataset consists of 14,640 tweets directed at several U.S. airlines. It serves as a valuable resource for sentiment analysis and natural language processing research, particularly in understanding customer satisfaction, airline service quality, and issues reported by travelers. Each tweet in the dataset is labeled with one of three sentiment categories: positive, neutral, or negative. Tweets labeled as negative are further categorized into specific negative sentiment reasons, such as late flight, customer service issue, canceled flight, and lost luggage, providing deeper insights into common complaints. The dataset also identifies the airline mentioned in each tweet, covering six major U.S. carriers: United Airlines, US Airways, American Airlines, Southwest Airlines, Delta Air Lines, and Virgin America. Additional metadata is provided for each tweet, including tweet ID, tweet text, tweet coordinates (if available), user information, and location data, allowing for further contextual analysis. The dataset is relatively small, with a total size of 8,46 MB, making it easily manageable for sentiment analysis tasks and machine learning applications. It includes 14,640 tweets from 7,700 unique users, providing a broad yet concise representation of customer interactions with airlines on Twitter. The tweets were collected over a one-month period in February 2015, offering a snapshot of public sentiment during that specific timeframe.