Machine learning techniques are essential for sentiment analysis

Most modern sentiment analysis systems are based on a machine learning technique called supervised learning. In this learning paradigm, the system is produced by a process referred to as "training" in which a training algorithm is applied to a large collection of inputs (text in this case) and desired associated outputs. The output of training is a model. The model is a mapping from text to its most likely label. Once the model is trained it can be used to classify new, unlabeled data without human input.

Today, deep learning models are by far the most common models used in supervised learning for natural language processing problems such as sentiment analysis. Deep learning is an umbrella term that covers a number of different techniques with the common trait that the model consists of a series of nonlinear vector-to-vector transformations of the input, until the output of the final vector transformation is used as input to a classification algorithm.

The training algorithm for deep learning models is called backpropagation. In short, the model learns to recognize patterns in the training data that are evidence of negative, neutral or positive sentiment.