AbstractAssociation rules are used to extract information from transactional databases with a collection of items also called “tokens” or “words.” The aim of association rule analysis is to indicate what and how items go with what items in a set of transactions called “documents.” This approach is used in the analysis of text records, of blogs in social media and of shopping baskets. We present here an approach to analyze documents using latent class analysis (LCA) clustering of document term matrices. A document term matrix (DTM) consists of rows referring to documents and columns corresponding to items. In binary weights, “1” indicates the presence of a term in a document and “0” otherwise. The clustering of similar documents provides stratified data sets used to enhance the interpretability of measures of interest such as lift, odds ratios and relative linkage disequilibrium. The article demonstrates the approach with two case studies. A first example consists of comments recorded in a survey aimed at pet owners. A second, much larger example, is based on online reviews to crocs sandals. Association rules describe combinations of terms in the pet survey and crocs reviews. In Section 3, we compute, for these case studies, association rule measures of interest defined in Section 2. We first introduce the case studies to motivate the methods proposed here. In Section 4, we provide a new approach with an enhanced interpretations of measures such as lift by comparing them across clusters derived from an LCA of the DTM. A key result is the application of clustered data in analyzing observational data. This enhances generalizability and interpretability of findings from text analytics. The article concludes with a discussion in Section 5.