Learn how to apply a Naïve Bayes classification model to solve a Natural Language Processing (NLP) problem in Python in this article.
Here are the steps we will cover:
Download a sample dataset
Split the dataset into test and train data
Vectorize the data
Build and measure the accuracy of the model
For example, we will use a publicly available dataset for spam detection with 5,572 SMS messages labeled as ham (legitimate) or spam. Here's how we'll approach it:
Step 1: Download the dataset from this site and extract the files.
Sample dataset: ham or spam?
Step 2: Import the text dataset and provide column names.
Step 3: Convert labels (ham and spam) to numbers (0 and 1).
Step 4: Split the dataset into test and train.
Step 5: Vectorize the data to convert words to numerical structures. You can read more on this here.
Step 6: Vectorize the training dataset.
Step 7: Vectorize the test dataset.
Step 8: Build the Naïve Bayes classification model. If you want to learn more about Naive Bayes, check out this post.
Step 9: Measure the accuracy on the test data.
I have used the codes from the following sites and modified wherever needed:
Further reference materials:
I personally found this post very helpful: https://www.ritchieng.com/machine-learning-multinomial-naive-bayes-vectorization/
You can find sample datasets on this site https://blog.cambridgespark.com/50-free-machine-learning-datasets-natural-language-processing-d88fb9c5c8da