Processing web evaluations through sentiment analysis

The importance of sentiment analysis technology in modern life is increasingly evident. However, the current sentiment analysis methods have different performances in practical applications, and their merits and demerits them are still debated by researchers. This paper summarizes and compares the dictionary-based approach and a variety of sentiment analysis techniques such as machine learning. Taking the data on Amazon as an example, this paper aims to find out the optimal method through empirical analysis and provide a reference for further research in this field. The results show that, except for the SentiWordNet method, which had an accuracy rate of less than 70%, all other tested methods, whether using machine learning, dictionary-based, or hybrid approaches, achieved accuracy rates between 80% and 95%. In this work, a hybrid method combining dictionary-based techniques for preprocessing reviews and machine learning for sentiment analysis was proposed. The hybrid method combines the advantages of both and shows higher accuracy and stability. This method not only provides a more reliable basis for consumers to make decisions when using online shopping platforms but also provides an effective feedback mechanism for sellers, which helps to increase customer trust and sales.