Research on Analyzing the Emotional Polarity of Malicious Swipe Comments on E-commerce Platforms Based on NPL

In the era of rapid advancements in natural language processing (NLP) models, these technologies have immense potential to detect and address societal issues, enhancing the functioning of the digital society. Online shopping platforms rely heavily on user reviews to influence buyer decisions, yet malicious reviews can significantly degrade user experience. This study focuses on analyzing the emotional polarity of malicious brushorder (falsely generated) reviews in e-commerce product comments, utilizing the Jingdong product review dataset. The methodology involves utilizing the Word2Vec model to vectorize the text data, followed by principal component analysis (PCA) for outlier detection to identify potential malicious reviews based on their unique characteristics. The PCA results are further leveraged for dimensionality reduction, simplifying the dataset. Subsequently, the BERT model is employed to perform semantic similarity analysis, allowing for the screening and expansion of the experimental dataset with similar malicious comments. This enriched dataset is then subjected to sentiment polarity analysis, enabling a deeper tinderstanding of the nature and impact of these malicious reviews. By facilitating buyers in making informed decisions based on genuine reviews, this research underscores the practical value of NLP hi addressing real-world challenges in e-commerce.