关键词: 在线评论, 时间序列聚类, K均值, 独立成分分析, 产品生命周期

Abstract:

For product, online word-of-mouse activity is a very typical index, which reveals life cycle evolution model of product. Understanding the product life cycle helps decision makers to make marketing strategy. It is more difficult to do clustering analysis because the product online comments are high-dimensional and complex. Thus, combining K-means algorithm with independent comment analysis and clustering products by this algorithm can improve the accuracy and reduce complexity in no small measure. Furthermore, in-depth analysis on the product life curve can effectively improve the effect of online word-of-mouth information in e-commerce marketing management and decision support, deepening the research on online reputation activity.

Key words: online reputation, time series clustering, K-means, Independent Component Analysis, life cycle