With the growth of the Internet, the ready accessibility and generation of online information has created the issue of determining how accurate or truthful that information is. The rapid speed of information generation makes the manual filter approach impossible; hence, there is a desire for mechanisms to automatically recognize and filter unreliable data. This research aimed to create a method for distinguishing vendor-sponsored reviews from customer product reviews using real-world online forum datasets. However, the lack of labelled sponsored reviews makes end-to-end training difficult; many existing approaches rely on lexicon-based features that may be easily manipulated by replacing word usages. To avoid this word manipulation, we derived a graph-based method for extracting latent writing style patterns. Thus, this work proposes a Contextualized Affect Representation for Implicit Style Recognition framework, namely CARISR. Transfer learning architecture was also adapted to improve the model’s learning process with weakly labeled data. The proposed approach demonstrated the ability to recognize sponsored reviews through comprehensive experiments using the limited available data with 70% accuracy.