[SOLVED] Hw assignment 2 (a2): deep learning cs6120

HuffPost (known as The Huffington Post until 2017) is a prominent website that provides a mix of news, commentary, and various forms of original content, including blogs and articles covering a wide range of topics from politics and business to entertainment and lifestyle.

The website initially featured contributions from unpaid bloggers from diverse fields such as politics, entertainment, and academia, amassing around 100,000 contributors by 2018. Over the years, the site has seen involvement from notable figures, including celebrities, politicians, and academics, who have contributed content, enhancing its reputation and reach. HuffPost has undergone significant transformations since its inception, expanding its content to include more news-focused articles and reducing reliance on its unpaid blogger program. It now features commissioned opinion pieces and personal essays. The website operates on a revenue model derived from advertising and maintains its content as free to access for users. Some fascinating history on mergers and acquisitions: The Huffington Post was acquired by AOL in March 2011 for $315 million, which expanded HuffPost’s reach and resources. In 2015, Verizon Communications acquired AOL, and HuffPost became part of Verizon Media. HuffPost rebranded in April 2017, including updates to its website design, logo, and content approach. In February 2021, BuzzFeed acquired HuffPost from Verizon Media in a stock deal, marking another significant shift in the platform’s ownership and business strategy.

You are assigned a classification task through a large dataset from HuffPost. Classification tasks lay the foundation for many applications that involve understanding, interpreting, and generating human language.

Some prominent examples of classification techniques include:

You are given more than 200k news items from HuffPost. The dataset is taken from a Kaggle competition:

The news articles in the dataset are each tagged with one of several categories (such as Politics, Technology, Entertainment, etc.). Each entry in the dataset typically includes the headline and a short description of the article, along with its assigned category/ The primary objective is to develop and compare various machine learning models to categorize news articles into predefined categories based on their headlines and short descriptions. This assignment will help you understand text classification nuances using traditional machine learning and deep learning approaches.

Optionally

Long Short-Term Memory (LSTM): Use LSTM to model dependencies in text sequences.

Please submit a fully executed jupyter notebook identifying question number and steps. Make sure to add comments to your solution.