Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
This work addresses the problem of identifying deceptive online opinions, which is important for improving trust in user-generated content, but it appears incremental as it builds on existing CNN methods with a specific enhancement.
The paper tackled deceptive opinion spam detection by optimizing a CNN model to incorporate word order characteristics, achieving more accurate detection results as demonstrated in TensorFlow-based experiments.
Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results.