Why is linear SVM good for text classification?

Text has a lot of features The linear kernel is good when there is a lot of features. That’s because mapping the data to a higher dimensional space does not really improve the performance. [3] In text classification, both the numbers of instances (document) and features (words) are large.

Why is text data linearly separable?

The higher the dimensionality, the easier it is to linearly separate data, as the VC dimension of a linear classifier in d dimensions is d+1 (e.g. see these slides). The VC dimension is the largest amount of points that a classifier can shatter (separate).

Why does linear SVM take so long?

The most likely explanation is that you’re using too many training examples for your SVM implementation. SVMs are based around a kernel function. Most implementations explicitly store this as an NxN matrix of distances between the training points to avoid computing entries over and over again.

Is text data linearly separable?

Most text categorization problems are linearly separable. Text categorization systems may make mistakes.

Why SVM is 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 sentiment analysis?

Support vector machine (SVM) is a learning technique that performs well on sentiment classification. Non-negative linear combination of multiple kernels is an alternative, and the performance of sentiment classification can be enhanced when the suitable kernels are combined.

Why linear kernel is used?

Linear Kernel is used when the data is Linearly separable, that is, it can be separated using a single Line. It is one of the most common kernels to be used. It is mostly used when there are a Large number of Features in a particular Data Set.

Does naive Bayes classifier better than SVM for sentiment analysis?

By seeing the above results, we can say that the Naïve Bayes model and SVM are performing well on classifying spam messages with 98\% accuracy but comparing the two models, SVM is performing better.

Is Naive Bayes good?

Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.