Sequential Text-Term Selection in Vector Space Models
提出一种顺序项筛选方法，在线性回归框架下处理向量空间模型中项集过大和维度灾难问题，通过按项长度划分子空间并顺序筛选，有效检测相关项。
Text mining has recently attracted a great deal of attention with the accumulation of text documents in all fields. In this article, we focus on the use of textual information to explain continuous variables in the framework of linear regressions. To handle the unstructured texts, one common practice is to structuralize the text documents via vector space models. However, using words or phrases as the basic analysis terms in vector space models is in high debate. In addition, vector space models often lead to an extremely large term set and suffer from the curse of dimensionality, which makes term selection important and necessary. Toward this end, we propose a novel term screening method for vector space models under a linear regression setup. We first split the entire term space into different subspaces according to the length of terms and then conduct term screening in a sequential manner. We prove the screening consistency of the method and assess the empirical performance of the proposed method with simulations based on a dataset of online consumer reviews for cellphones. Then, we analyze the associated real data. The results show that the sequential term selection technique can effectively detect the relevant terms by a few steps.