Improving Product Competitiveness of Tablet Computers Based on Data Mining

This study explores strategies to enhance the competitiveness of tablet computers through data mining of user reviews. We utilize TF-IDF and Doc2Vec to vectorize textual feedback, combined with SVM classification and both K-means and hierarchical clustering methods. Using reviews from 14 tablet models collected from JD.com, the study identifies distinct user demand segments: performance-oriented, experiencepriority, and service-sensitive users. Our findings suggest that integrating text mining with machine learning not only aids in identifying consumer demand patterns but also enables targeted product optimization. The paper concludes with actionable suggestions for product innovation based on differentiated user feedback.