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Feature engineering is the construction or extraction of features from data. In this section, we analyze and discuss some of the commonly used features in the domain of review spam detection. As briefly outlined in the introduction, previous studies have used several different types of features that can be extracted from reviews, the most common being words found in the review's text. This is commonly implemented using the bag of words approach, where features for each review consist of either individual words or small groups of words found in the review's text. Less frequently, researchers have used other characteristics of the reviews, reviewers and products, such as syntactical and lexical features [11] or features describing reviewer behavior. The features can be broken down into the two categories of review and reviewer centric features. Review centric features are features that are constructed using the information contained in a single review. Conversely, reviewer centric features take a holistic look at all of the reviews written by any particular author, along with information about the particular author. Competing interests

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