TAF PUBLISHING

In the ever-evolving landscape of information and communication, text mining plays a pivotal role in extracting valuable insights from vast textual data. While sentiment analysis has garnered substantial attention, this research delves into the broader spectrum of text mining, aiming to uncover emerging trends, challenges, and contemporary approaches that extend beyond traditional sentiment analysis. The study begins by scrutinizing the background of sentiment analysis in capturing the nuanced landscape of language, prompting an exploration into the types and classifications and compiling the available work of sentiment analysis based on text from the lexical approach to the deep learning approach. Researchers and practitioners grapple with multifaceted challenges that involve navigating the complexities of context, sarcasm, and ambiguity. This underscores the necessity for more advanced methodologies to effectively address the evolving intricacies of language. By synthesizing insights from the analysis of current trends and challenges, this research contributes to the ongoing dialogue in text mining, offering a comprehensive perspective beyond sentiment analysis. The findings of this study are anticipated to inform researchers, practitioners, and industry professionals in navigating the intricate landscape of text mining, fostering innovation and responsible deployment in an increasingly data-driven society.