Since the introduction of the lasso in regression, various sparse methods have been developped in an unsupervised context like sparsePCA which is a combination of feature selection and dimension reduction. Their interest is to simplify the interpretation of the pseudo principal components since each is expressed as a linear combination of only a small number of variables. The disadvantages lie on the one hand in the difficulty of choosing the number of non-zero coefficients in the absence of a criterion and on the other hand in the loss of orthogonality properties for the components and/or the loadings. In this paper we are interested in sparse variants of correspondence analysis (CA) for large contingency tables like documents-terms matrices. We use the fact that CA is both a PCA (or a weighted SVD) and a canonical analysis, in order to develop column sparse CA and rows and columns doubly sparse CA.