Comment Analysis of Online Shopping Based on Big Data and NLP Techniques
Dongping Sheng*, Zhongyuan Ma, Haidong Feng, Chenqi Zhou, Hao Liu, Hun Guo, Chun Wei Su · International Scientific Technical and Economic Research · 2025
With the rapid development of online shopping, the number of consumers has increased significantly, and user reviews have become increasingly influential on sellers and brands. User reviews not only provide feedback on products and services, but also provide companies with important market insights. Therefore, review analysis has become a crucial research field. With the help of big data, artificial intelligence (AI) and natural language processing (NLP) technologies (such as keyword extraction, sentiment analysis, etc.), valuable information can be effectively extracted from massive consumer reviews. To this end, we designed a mathematical model based on ASIN (Amazon Standard Identification Number) for in-depth analysis of product review text. Through ASIN, relevant data on the Amazon website, including product names, categories and other information, are obtained, and the data is cleaned, classified and processed. Finally, we generate word clouds and construct relationship network diagrams to show the potential patterns and connections in text data, providing data support and visual analysis for product and market decisions. **************** ACKNOWLEDGEMENTS**************** This work is supported by ministry of education industry-university cooperative education project (Grant No.: 231106441092432), the research and practice of integrating "curriculum thought and politics" into the whole process of graduation design of Mechanical engineering major: (Grant. No.: 30120300100-23-yb-jgkt03), research on the integration mechanism of "course-training-competition-creation-production" for innovation and entrepreneurship of mechanical engineering majors in applied local universities (Grant. No.: CXKT202405), Mechanical manufacturing equipment design school-level "gold class" construction project (Grant. No.: 30120324001).