Abstract
The integration of technology into everyday life has driven an exponential increase in data creation and management. This surge in data, particularly human-generated in forms such as text, audio, and video, has led to a growing interest in automated methods to extract useful information from large volumes of unstructured data. Text analysis, encompassing techniques like data mining, machine learning, and computational linguistics, has emerged as a crucial tool for extracting information and patterns from textual data. This paper explores various methodologies in text analysis, including sentiment analysis, information extraction, natural language processing (NLP), text classification, and deep learning. Each methodology is examined for its applications, particularly in domains like social media, biomedical fields, e-commerce, healthcare, and agriculture.