Classification of News HeadLines

News Headline Classification through multiple machine learning model and comparison of results.

Models implemented:

• Multinomial Naive Bayes
• Support Vector Machines
• Neural Network with Softmax Layer

Metrics used to evaluate the performance of models:

• Accuracy
• Precision
• Recall
• F1 Score

We evaluate each classifier's ability to select the appropriate category given an article’s title and a brief article description. The confusion matrix is created to explore the results and calculate the metrics.

Feature Extraction Techniques:

The collection of text documents is converted to a matrix of token counts using count vectorize that produces a sparse representation of the counts.

TFIDF,term frequency–inverse document frequency, is the statistic that is intended to reflect how important a word is to a document in our corpus. This is used to extract the most meaningful words in the Corpus. corpus ( Collection of written texts,words etc. )

Our Datasets is a collection of datasets of short text fragments that we used for the evaluation of our topic-based text classifier. This is a dataset of ~32K english news extracted from RSS feeds of popular newspaper websites (nyt.com, usatoday.com, reuters.com). Categories are: Sport, Business, U.S., Health, Sci&Tech, World and Entertainment.

DataSet is Included in the project Folder as news (raw data)

Packages required:

• Pandas
• sklearn
• Numpy

Steps to Run :

1.Open Cmd
2.Open Jupyter Notebook by Typing Jupyter Notebook in cmd. (See Installation details for Jupyter if not already Installed!!)
3.open News Classification.ipynb
4.Run the program blocks one at a time for clear visuallization and categorization of our implementation for classifying news headlines
link for docs ![https://docs.google.com/document/d/1VptDVOIk1nrRSKbRwtTzWb26rauhiZwLNMaqw4zOfcw/edit?usp=sharing]

Contributions:

Algorithm Implementation

• Neural Network-Abhijeet Saraf
• Multinomial Naive Bayes-Vishrut Vats
• Support Vector Machines-Abhishek Kumar Mandal

Documentation:

• Abhijeet Saraf and Vishrut Vats

Debugging:

• Abhishek Kumar Mandal and Vishrut Vats

Visualization:

• Abhijeet Saraf