The advent of the Internet has led to profound social changes, altering decision-making processes and social dynamics. However, this digital transformation comes with challenges, including the circulation of bad news that creates hopelessness. In response, sentiment analysis, a facet of natural language processing, has emerged as an influential tool for classifying media into positive, negative, or neutral categories. Our research highlights the contribution of sentiment analysis to classifying news content and reviews methods and models. We introduce a methodology encompassing data collection, preprocessing, and the use of models including BERT, XLM-RoBERTa, mBERT, DistilBERT, and XLNet. Using a dataset of 3000 labeled news stories, we train and evaluate sentiment analysis models. Data preprocessing involves steps such as tokenization, encoding, label encoding, and creating efficient data loaders using PyTorch. Metrics like precision, accuracy, recall, and F1 score give a well-rounded evaluation of the model's performance. In summary, this research highlights the significance of sentiment analysis in tackling negative content and provides valuable insights for making informed decisions in today's digital landscape.