Detecting deceptive opinions with profile compatibility

We propose using profile compatibility to differentiate genuine and fake product re-views. For each product, a collective profile is derived from a separate col-lection of reviews. Such a profile con-tains a number of aspects of the prod-uct, together with their descriptions. For a given unseen review about the same product, we build a test profile using the same approach. We then perform a bidi-rectional alignment between the test and the collective profile, to compute a list of aspect-wise compatible features. We adopt Ott et al. (2011)’s op spam v1.3 dataset for identifying truthful vs. decep-tive reviews. We extend the recently pro-posed N-GRAM+SYN model of Feng et al. (2012a) by incorporating profile compat-ibility features, showing such an addition significantly improves upon their state-of-art classification performance.