What type of learning method does sentiment analysis typically employ?

Sentiment analysis primarily employs supervised learning because it requires labeled data to train the model effectively. In supervised learning, algorithms are fed with input-output pairs, where the input is the text data and the output is the labeled sentiment (such as positive, negative, or neutral). The model learns from these examples during training, enabling it to predict sentiments on new, unseen texts.

This approach is key in sentiment analysis, as the accuracy of the predictions depends significantly on the quality and quantity of the labeled data used during the training phase. In contrast, unsupervised learning does not involve labeled outcomes and would not be suitable for discerning sentiment without predefined categories. Reinforcement learning focuses on learning through rewards and penalties and is not typically employed for text classification tasks like sentiment analysis. Deep learning represents a type of method that can be applied within supervised learning frameworks, but the fundamental approach to sentiment analysis is primarily rooted in supervised techniques, due to the need for understanding the sentiment associated with specific data points.