Performing sentiment analysis
The power of sentiment analysis lies in its ability to uncover the emotions behind text data, providing invaluable insights into customer sentiments. While the focus of the Twitter Airline Sentiment dataset is on categorizing sentiments into positive, negative, and neutral classes, sentiment analysis can also extend beyond these basic categories. Depending on the application, sentiments can be analyzed to detect more nuanced emotional states or attitudes, such as happiness, anger, surprise, or disappointment.
Building your own ML model
A fundamental aspect of training sentiment analysis models, especially with traditional NLP techniques, is the necessity for pre-labeled data. These labels are typically derived from human annotations, a process that involves individuals assessing the sentiment of a piece of text and categorizing it accordingly. The sentiment scores in this Twitter dataset were collected with the help of volunteers, and some of...
