What type of model is trained to determine sentiment based on predefined emotional categories?

A classification model is the appropriate choice for determining sentiment based on predefined emotional categories. This type of model is specifically designed to categorize input data into one of several defined classes or labels. In the context of sentiment analysis, these categories might include positive, negative, and neutral sentiments, among others.

Classification models are trained on labeled datasets, where each input is associated with a specific emotional category. As the model learns from this data, it develops the ability to predict the sentiment of new, unlabeled data points. This process is useful in a variety of applications, such as analyzing customer reviews or social media posts to gauge public opinion or emotional reactions.

In contrast, generative models focus on generating new data from learned distributions and are not inherently designed for categorization tasks. Regression models, on the other hand, are used for predicting continuous values rather than class labels, making them unsuitable for sentiment analysis. Clustering models aim to group similar data points without any predefined labels, which does not align with the goal of categorizing sentiment into specific emotional categories.