A Two-Stage Feature Selection Method for Text Categorization by Using Category Correlation Degree and Latent Semantic Indexing

With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.

The effects of person-organization fit on lending behaviors:Empirical evidence from Kiva

Donation-based crowdfunding,as part of impact investment,plays a vital role in promoting economic development and alleviating poverty.In order to enhance the lender's enthusiasm for lending behavior,some platforms,for example Kiva,have introduced groups to facilitate lending.This study examines how the group environment can affect the lenders’behaviors in crowdfunding.It has been found that lenders who join groups contribute 1.2 more loans(about$30-$42)per month than those who do not,but the theoretical mechanism of these differences is unclear.To understand in depth how the group environment affects lending behaviors,we introduce and develop the PersonOrganization fit theory and Free-rider theory in this study.Combining machine-learning techniques with empirical analysis,the results show that the matching degree of motivation between group and lender has a positive effect on the lender behavior,i.e.,lending to loans,and this relationship is weakened by free-riding in large groups.In addition,the group openness can have different effects on lenders of different group sizes.Our research enriches the existing crowdfunding literature and fills the gap in the research on new lending models in crowdfunding,and it will also be useful for crowdfunding platforms in setting the rules for building groups.