Sentiment Analysis on Text data: Methods, Applications, Challenges and Future Directions

Sentiment analysis, or opinion mining, is the computer examination of text data thoughts, opinions, and emotions. Emotions analysis has gained popularity because to the growing volume of user-generated content on social media and the need to understand public mood and client feedback. This review article summarizes sentiment analysis's knowledge, methods, challenges, and advancements. Introduce sentiment analysis and its usefulness in many sectors before discussing how sentiment analysis approaches have grown from rule-based systems to machine learning and deep learning. Next, discuss how sentiment analysis improves politics, business, marketing, social media monitoring, and recommendation engines. This survey research examines the latest sentiment analysis methods, including supervised learning models, unsupervised methods, lexicon-based methods, and hybrid models. Examine each approach's pros and cons and applicability for different scenarios. Sarcasm and irony identification, context-dependent sentiment, noisy and unstructured text data, and multilingual sentiment analysis are all discussed. The methods, challenges, and advances of sentiment analysis are covered in this comprehensive study. It encourages innovation and cooperation in text data sentiment analysis by providing a comprehensive resource.