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    An alternative classification framework was proposed by Li et al. [25]. In their work they argued that existing supervised learning algorithms in literature are usually narrowed to one specific domain and rely heavily on domain-specific vocabulary. To address this, they tried to improve our understanding of the nature of deceptive reviews by creating a cross domain dataset that included three types of reviews from three domains (hotel, restaurant and doctor). AMT was used to solicit fake reviews; however an additional set of fake reviews was solicited from "domain experts". Truthful reviews were collected from review websites. Their classification framework was based on using the Sparse Additive Generative Model (SAGE), which is a generative Bayesian approach introduced by Eisenstein et al. [30]. Basically, it is "a Bayesian generative approach that can capture the multiple generative facets (i.e., deceptive vs. truthful, positive vs. negative, experienced vs. non-experienced, hotel vs. restaurant vs. doctor)" [25]. The authors used a combination of topic models (statistical models for discovering abstract topics in a collection of documents) and generalized additive models (linear models in which the linear predictor is dependent on unknown, smoother functions) generated using SAGE as well as SVM in their classification experiments. Additionally, they investigated different methods of feature engineering and found the use of more general features, such as LIWC and POS, to be more robust than unigram features alone when modeled using SAGE for cross-domain classification; however, when comparing the intra-domain classification (i.e., hotels reviews only) the best performance is achieved by unigram features. This indicates that different linguistic features may appear in different domains, and more robust cues of deceptive opinion spam need to be identified if a cross domain classifier is to be created. Of note was that the classifier exhibited particular difficulty when trained using the restaurant and hotel reviews and evaluated against the doctor reviews. Using SAGE, accuracies of 64.7 and 63.4 % were achieved using LIWC and POS tag features respectively but only 52.0 % when using Unigram features. About this article

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