Sentiment Analysis on Amazon Food Reviews

Sentiment analysis has become an important tool for understanding consumer behavior, especially on e-commerce platforms like Amazon. This study focuses on the use of sensory analysis methods in Amazonian food research, highlighting the strengths and limitations of traditional and modern methods Term-based translations such as the NLTK VADER have gained popularity because of for its simplicity and speed, but often capture complex linguistic nuances such as sarcasm or contextual emotion as opposed to its inability, transformer-based models, especially Hugging Face's RoBERTa, for information relevant deeper understanding and uses big data and advanced deep learning techniques to significantly improve accuracy in sensory classification This study shows that RoBERTa As it outperforms dictionary based methods in domain -specific and complex emotions in handling, well suited for large-scale, real-world data such as customer reviews The paper emphasizes the importance of selecting an appropriate sentiment analysis method based on dataset complexity and size.