Is SVM good for text classification?

Is SVM good for text classification?

Furthermore, SVMs do not require any parameter tuning, since they can find good parameter settings automatically. All this makes SVMs a very promising and easy-to-use method for learning text classifiers from examples.

Is neural network good for text classification?

It’s not difficult to use Scikit-learn to build machine-learning models that analyze text for sentiment, identify spam e-mails, and classify textual data in other ways. But state-of-the-art text classification is most often performed with neural networks.

Why is SVM better than naive Bayes for text classification?

The biggest difference between the models you’re building from a “features” point of view is that Naive Bayes treats them as independent, whereas SVM looks at the interactions between them to a certain degree, as long as you’re using a non-linear kernel (Gaussian, rbf, poly etc.).

Why is SVM good for NLP?

the solution to the original NLP problem. SVM is an optimal classifier in the sense that, given training data, it learns a classifica- tion hyperplane in the feature space which has the maximal distance (or margin) to all the training examples (except a small number of examples as outliers) (see e.g. [9]).

Why are neural networks better than SVM?

An SVM possesses a number of parameters that increase linearly with the linear increase in the size of the input. A NN, on the other hand, doesn’t. Even though here we focused especially on single-layer networks, a neural network can have as many layers as we want.

Which is the best model for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

Is decision tree better than SVM?

SVM uses kernel trick to solve non-linear problems whereas decision trees derive hyper-rectangles in input space to solve the problem. Decision trees are better for categorical data and it deals colinearity better than SVM.

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