Paper Title
A Comparative Study on Developments in Sentiment Analysis and News Text Classification

Abstract
A detailed examination of the diverse methods and techniques employed in the realm of text categorization, specifically in relation to news articles, is expounded upon in this survey. The study encompasses investigations employing distinct machine learning models, deep learning algorithms, and sentiment analysis techniques for the purpose of classifying and scrutinizing news data. This research concentrates on various numerical approaches, including Long Short-Term Memory and BERT, alongside more traditional methods like Latent Dirichlet Allocation, Support Vector Machines, and Regression. It also explores how these strategies can be applied across different fields like online news platforms, financial markets, and public governance. The primary objective of this study is to demonstrate the potential of the provided methodologies in augmenting clas- sification precision, diminishing intricacy, and amplifying user satisfaction. The study investigates their adaptability to different contexts and their potential to enhance decision-making processes in practical scenarios. Keywords - Text Classification, News Text Categorization, LDA, Random Forest Algorithm, Text Summarizing, Bayesian Networks, SVM, Machine Learning Models, Bert Model, News Article Classification, DL, Stock Market Forecasting, Sentiment Analysis.