Vectors are (sometimes huge) lists of numbers which represent a set of coordinates in some space. ... So, provided we can find vector representations which encode as much information from our texts as possible, we will be able to apply the SVM algorithm to text classification problems and obtain very good results.
How does SVM improve text classification?
I used several practices to improve the results of my model.
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Introduction
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Introduction
- Domain Specific Features in the Corpus. ...
- Use An Exhaustive Stopword List. ...
- Noise Free Corpus. ...
- Eliminating features with extremely low frequency. ...
- Normalized Corpus. ...
- Use Complex Features: n-grams and part of speech tags.
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.).
