A Comprehensive Approach to Sentiment Analysis with Natural Language Processing Techniques

The expansion of social networking in modern times has made sentiment analysis an integral component of natural language processing. Polarity in online shopping evaluations is the most reliable indicator of how to obtain honest feedback from clients. As a result, evaluating the overall sentiment of product reviews posted on online sites is essential. Amazon's e-commerce electronic product reviews present unique sentiment analysis challenges due to concerns such as dimension mapping, sentiment word disambiguation, and word polysemy. This paper proposes the sentiment analysis model Ernie-Bi-LSTM to address these concerns. Bidirectional long-term, short-term memory uses the knowledge integration word embedding approach to extract text from the dynamic word vector for better representation. The proposed model achieves an accuracy of 94.91%, which is superior to classic deep learning models. Furthermore, it provides valuable insights into the future of this dynamic and ever-changing scientific field.