  Sentiment analysis (SA) is the automated process of detecting and
understanding the emotions conveyed through written text. Over the past decade,
SA has gained significant popularity in the field of Natural Language
Processing (NLP). With the widespread use of social media and online platforms,
SA has become crucial for companies to gather customer feedback and shape their
marketing strategies. Additionally, researchers rely on SA to analyze public
sentiment on various topics. In this particular research study, a comprehensive
survey was conducted to explore the latest trends and techniques in SA. The
survey encompassed a wide range of methods, including lexicon-based,
graph-based, network-based, machine learning, deep learning, ensemble-based,
rule-based, and hybrid techniques. The paper also addresses the challenges and
opportunities in SA, such as dealing with sarcasm and irony, analyzing
multi-lingual data, and addressing ethical concerns. To provide a practical
case study, Twitter was chosen as one of the largest online social media
platforms. Furthermore, the researchers shed light on the diverse application
areas of SA, including social media, healthcare, marketing, finance, and
politics. The paper also presents a comparative and comprehensive analysis of
existing trends and techniques, datasets, and evaluation metrics. The ultimate
goal is to offer researchers and practitioners a systematic review of SA
techniques, identify existing gaps, and suggest possible improvements. This
study aims to enhance the efficiency and accuracy of SA processes, leading to
smoother and error-free outcomes.
