Practical example: Twitter Airline Sentiment dataset

Our exploration of sentiment analysis will be based on the Twitter Airline Sentiment dataset. This collection of tweets directed at various airlines provides a rich dataset for understanding how sentiment analysis can be applied to real-world marketing challenges.

Here, sentiments are classified as positive, negative, or neutral via human annotation, reflecting a range of customer emotions from satisfaction to frustration.

Source code and data: https://github.com/PacktPublishing/Machine-Learning-and-Generative-AI-for-Marketing/blob/main/ch.5/SentimentAnalysis.ipynb

Data source: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment

Conveniently, this dataset contains not only tweets and their sentiment classifications but also, in some cases, explanations for tweets with negative sentiment. These will provide useful benchmarks for us to evaluate the approaches we will develop in this...