This document outlines a procedure to classify news articles into categories such as entertainment, sports, business, health, and technology using a naive Bayes classifier. The procedure involves collecting pre-labeled news articles from sources like Google News, training a naive Bayes model on the labeled data, and then using the trained model to classify unlabeled articles by predicting the most probable category based on learned probabilities from the training set. It also explores using k-means clustering to group the articles during the classification process.

1. News Articles Classification
14101,14108
Mtech. Computer Science
Part 1
DCST, Goa University

2. Outline

3. Aim
 To classify the news article's
according to the labels such as
Entertainment, Sports, Business,
Health and Technology.
 Categorize news articles into
predetermined Labels without
predefined feature vectors.

4. Procedure
 Data collection
◦ News articles are taken from pre labeled
groups such as Google News.
◦ Some pre-processing is also done on data.
 Training Phase
◦ Naive based classifier is used to build a
model in the training phase
 Testing Phase
◦ Given some unlabeled articles, naive based
classifier classifies it on the basis of learned
probabilities.

5. Clustering
 We use K-means clustering algorithm
to cluster the articles into groups.

6. Thank You