Youth-led media is any effort created, planned, implemented, and reflected upon by young people in the form of media, including websites, newspapers, television shows, and publications. Such platforms connect writers, artists, and photographers in the age range of 13–24 all around the globe and promote and defend a free youth press. Members of these platforms not only have the freedom to express their own opinions on various issues and topics but also represent various communities and let their voices be heard.

Hence, such platforms prove to be a good source of data to understand and analyze youth aspirations across various parts of the globe. In the remaining sections, we will explain our methodology of data collection and will list down our results and insights derived from the analysis of various topics.

What does the section talk about?

This Section is overall given insights about the data was distributed over newspapers and articles, the insights and visualizations tell us about how youths are going on and how their sentiments change overtime period (Ranges from 2015–2020)

Why did we choose this topic?

This topic aims to analyze data from a different perspective i.e Outside Social media. This is the reason we choose this topic to scrape and analyze the data i.e present over there outside social media and we present our insights accordingly.

Objectives

• To scrape and process News articles from different resources, to prepare it for sentiment analysis and topic modeling, in order to draw useful insights about the sentiment of the youth from it.
• To conduct sentiment analysis, for understanding the youth sentiment better.
• To collect the insights from all of these points and to visualize the results in a cogent manner for the audience.

Methodology

Data Collection

To collect articles, we scraped data from various media platforms (ref. Table 1) using a scraper we made using BeautifulSoup and requests a library in Python. Lots of articles were scraped ranging from the year 1994 to 2020 and merged to a final dataset that we used for analysis. We also focused on extracting articles for certain categories, viz:

• Education
• Environment & Climate
• Human Rights
• COVID-19
• Politics
• Health and Leisure

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