Naïve Bayes Classification Model for Natural Language Processing Problem using Python

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.


Further reference materials:

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