I asked my self this simple question while reading "Comment Abuse Classification with Deep Learning" by Chu and Jue. Indeed, they say at the end of the that
It is clear that RNNs, specifically LSTMs, and CNNs are state-of-the-art architectures for sentiment analysis
To my mind CNNs were only neurons arranged so that they correspond to overlapping regions when paving the input field. It wasn't that recurrent at all.
You are right. I think you are just misinterpreting the part of the sentence ('specifically LSTMs'). LSTMs are an example of a popular type of RNN. RNNs and CNNs are different architectures but they can be used together.
Here is another sentence with the same structure:
It is clear than dogs, specifically corgis, and cats are very common in online memes.
Both CNN and RNN fall into the super set of neural networks,however applications of the two matters.
So to branch them off in terms of applications,
I would say CNN’s are mainly used for vision related applications, whereas, RNN’s are mainly used for language processing applications.
You can refer to these links for further details.
Comparative Study of CNN and RNN for Natural Language Processing
How are recurrent neural networks different from convolutional neural networks? The unreasonable effectiveness of Recurrent Neural Networks
Hope this can give you a glimpse!
