The essential use of natural language processing is to analyze the sentiment of the author
via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying
emotion in the context. It has been used in several subject areas such as stock market prediction, social
media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc.
Many researchers have worked on these areas and have produced significant results. These outcomes
are beneficial in their respective fields, as they help to understand the overall summary in a short
time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such
as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of
the essential studies done so far and to provide an overview of SA models in the area of emotion
AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA
along with machine learning models that are used to analyze the sentiment of the given context.
Furthermore, this work also discusses di erent neural network-based approaches for analyzing
sentiment. Finally, these di erent approaches were also analyzed with sample data collected from
Twitter. Among the four approaches considered in each domain, the aspect-based ontology method
produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85%
accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved
90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/
