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