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It has been proved that CNN (convolutional neural network) is quite useful for text/document classification. I wonder how to deal with the length differences as the lengths of articles are different in most cases. Are there any examples in Keras? Thanks!!
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Based on the above characterization, it makes sense to choose a CNN for classification tasks like sentiment classification since sentiment is usually determined by some key phrases and to choose RNNs for a sequence modeling task like language modeling or machine translation or image captioning as it requires flexible ...
Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification.
Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79% which is 5% improvement over Naive Bayes.
Here are three options:
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