The main component that differentiates Recurrent Neural Networks (RNN) from the other models is the addition of a loop at each node. This loop brings the recurrence mechanism in RNNs. In a basic Artificial Neural Network (ANN), each input is given the same weight and fed to the network at the same time. So, for a sentence like “I saw the movie and hated it”, it would be difficult to capture the information which associates “it” with the “movie”.
The addition of a loop is to denote preserving the previous node’s information for the next node, and so on. This is why RNNs are much better for sequential data, and since text data also is sequential in nature, they are an improvement over ANNs.