Paper Contents
Abstract
The project investigates the relationship between news article sentiments and their popularity. The project involves a structured approach encompassing data collection, data preprocessing, and analysis.The initial phase, encapsulated and focuses on gathering relevant news articles from online sources, ensuring the data encompasses a wide array of news topics. The cleaning.py script outlines the preprocessing phase, where raw data undergoes rigorous cleaning to remove inconsistencies, redundant information, and noise, preparing it for subsequent analysis. This step is crucial for enhancing the quality and accuracy of the sentiment analysis.The core analytical work, detailed includes applying sentiment analysis algorithms to evaluate the tone and sentiment polarity of news articles. Various data visualization techniques are employed to interpret the sentiment distribution and its correlation with indicators of popularity. The results offer insights into how the sentiment of news content impacts its likelihood of being widely shared or viewed.
Copyright
Copyright © 2024 Rahul Tomar. This is an open access article distributed under the Creative Commons Attribution License.
