Training the baseline model

In this section, we will look at the code that helps us perform actual training on the training dataset. We will look at the implementation first, and then I will explain the code step by step. Here, we will be implementing Naive Bayes and SVM algorithms. For implementation, we will be using the scikit-learn library. You can find the code at this GitHub link: https://github.com/jalajthanaki/Sentiment_Analysis/blob/master/Baseline_approach.ipynb.

Implementing the baseline model

In order to understand the implementation of the baseline model, you can refer to the following code snippet:

Figure 5.8: Code snippet for performing training using naive Bayes and SVM

We have implemented the following four algorithms here:

• Multinomial naive Bayes

• C-support vector classification with kernel rbf

• C-support vector classification with kernel linear

• Linear support vector classification

Multinomial naive Bayes

As you can see in the preceding code snippet, we have used Multinomial naive...