Data Analysis of Product Information and Reviews on E-Commerce Platforms Based on Python

In the digital environment, e-Commerce platforms have accumulated a large amount of both structured and unstructured data. As a core carrier of user perception, review content has a significant impact on consumer behavior and platform operations. This paper, based on Python programming, relies on multi-source review samples to construct a multi-level analytical framework. It combines sentiment annotation, behavioral variable extraction, and time series modeling to systematically explore the emotional characteristics and behavioral patterns within the reviews. Machine learning methods are employed to improve the accuracy of text classification, and trend prediction models are built to identify the evolution path of sentiment. Experimental results show that negative emotions possess stronger behavioral guiding power in user feedback, and the models perform stably in both classification and prediction tasks. This study provides data support for platforms to optimize user experience, enhance public opinion monitoring, and formulate marketing strategies.