Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network

Product reviews are becoming a more popular tool for businesses and individuals when making judgements about purchases. Spammers create synthesized reviews to either promote certain items or denigrate those of rivals to make money. As a result, in recent years, both the business and research sectors have paid close attention to the detection of false opinion spam. Customers' decision-making is severely harmed by false opinion spam in service or product evaluations. It's becoming difficult to identify false opinion spam. Accordingly, the article proposed to detect deceptive opinion spam based on a hybrid deep learning technique. Initially, the model was tested using deceptive reviews gathered from several online forums. To identify deceptive reviews, many researchers at the moment create models based on a single text attribute. On the contrary, deceptive reviewers will decisively copy the wording style of legitimate evaluations while submitting reviews. These text-feature-based techniques may or may not be successful. As a result, the research suggested an ensemble multiple-feature selection technique of the Extra tree classifier to extract information based on a variety of features, including text, behaviour, and deceptive scoring features. In addition, a data resampling approach is used that integrates the Borderline-SMOTE algorithm to reduce the effects of the high dimensional imbalanced class category distribution. For detecting deceptive reviews, the article developed a hybrid technique of Bidirectional Long Short-Term Memory (Bi-LSTM) with a Capsule Neural Network to detect the positive and negative false opinions spam. The model optimizes the dynamic routing algorithm and changes the structure of the conventional capsule network without sacrificing classification performance, leading to high model accuracy. The model performance is evaluated using Python software. The study assesses the suggested model using data from two distinct domains (hotel and restaurant) as a standard benchmark. The experimental results demonstrate the advantage of neural models with higher accuracy of 99% respectively, showing that the suggested neural model greatly outperforms the state-of-the-art techniques.