What is the difference between CNN and RNN?

 In the context of data science, for the objective of deciding between using convolutional neural networks or a recurrent neural networks architecture for sentiment analysis of a movie review, you can use several factors for choosing between two:-

Sequence length

If the length of the text sequence varies differently then you can use the RNN as it can offer variable length sequence.

Temporal dependencies

The RNN inherently captures the temporal dependencies in sequential data. It would make them well-suited for tasks where the order of words matters. CNN is better suited for this particular task or feature.

Model complexity

CNN is considered simpler and computationally more effective as compared to RNN, which would help in easier the train and deployment.

Data size

If you have a large dataset, then CNN might perform well as compared to RNN.

Here is a Python example given by using Tensorflow for illustration of how you can implement CNN-based and an RNN-based sentiment analysis model:-

Import tensorflow as tf

From tensorflow.keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, LSTM, Dense

From tensorflow.keras.models import Sequential

# Define CNN-based sentiment analysis model

Def create_cnn_model(input_dim, max_seq_length):

    Model = Sequential([

        Embedding(input_dim=input_dim, output_dim=128, input_length=max_seq_length),

        Conv1D(filters=128, kernel_size=5, activation=’relu’),

        GlobalMaxPooling1D(),

        Dense(1, activation=’sigmoid’)

    ])

    Return model

# Define RNN-based sentiment analysis model

Def create_rnn_model(input_dim, max_seq_length):

    Model = Sequential([

        Embedding(input_dim=input_dim, output_dim=128, input_length=max_seq_length),

        LSTM(units=128, dropout=0.2, recurrent_dropout=0.2),

        Dense(1, activation=’sigmoid’)

    ])

    Return model

# Example usage

Input_dim = 10000  # Example vocabulary size

Max_seq_length = 100  # Example maximum sequence length

# Create CNN-based model

Cnn_model = create_cnn_model(input_dim, max_seq_length)

Cnn_model.summary()

# Create RNN-based model

Rnn_model = create_rnn_model(input_dim, max_seq_length)

Rnn_model.summary()