Keyword Analysis & Research: svm for text classification sklearn

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Frequently Asked Questions

What is the best text classification algorithm?

We achieved 83.5% accuracy. Let’s see if we can do better with a linear support vector machine (SVM) , which is widely regarded as one of the best text classification algorithms (although it’s also a bit slower than naïve Bayes). We can change the learner by simply plugging a different classifier object into our pipeline:

How do I learn text classification?

There is not much out there to help those who are new to natural language processing and text classification algorithms. Learning Text Classification typically requires researching many articles, books, and videos. This is my take on explaining the Text classification technique with just the right content to get you working.

Can I print vectorized data for classification analysis?

Additionally, you can directly print the vectorized data to see how it looks: Consequently, our data sets are ready to be fed into different classification Algorithms.

What are the different types of word vectorization techniques?

Word Vectorization techniques such as Count Vectorizer and Word2Vec. Parameter tuning with the help of GridSearchCV on these Algorithms. Other classification Algorithms such as Linear Classifier, Boosting Models and even Neural Networks.

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