Abstract:
Fake news is spreading increasingly across social media and online platforms in
a troubling manner, posing a crucial and concerning challenge for our so-ciety.
Detecting misleading content and analyzing the sentiments it conveys remains
a formidable challenge. This article focuses on the use of different sentiment
analysis techniques to detect fake news, specifically by using ma-chine-learning
algorithms to identify fake news in texts with the goal of dis-tinguishing truth
from falsehood. By combining polarity analysis with ma-chine learning
techniques, we observe a marked improvement in detecting deceptive content.
The results obtained across various datasets show that sentiment analysis has
significantly enhanced detection scores, achieving an accuracy of 99.8%.
