Design And Implementation Of Spam Review Detection System Based On Deep Learning Algorithm

With the rapid development of the Internet, review site palys a more and more important role on the shopping in the Internet. There are shortcomings such as low accuracy and week robustness in existing spam review detection system. This paper conduct a spam review detection system based on deep learning algorithm to improve the accuracy of the recognition and the flexibility of the system.To overcome the shortcomings, this paper applies a novel system design, which depart the system into training module and detection module.Training module faced to system designer and it will training the spam review detection model that we propose. Detection module is for user,which is used to help user to identify the spam review.To implement the above mentioned functions, the training model is divided into five parts: data crawling submodule, data dealing submodule,model construction submodule, model training submodule and model testing submodule. We crawl the data from Yelp.com and deal those data to mathematics representation. We utilize the building model and the training dataset to train model. At last, we use the learned model to test in the test dataset. For the construction of the spam review detection model,considering the data consist of context and metadata, we introduce the LSTM(Long-Short Term Memory) and CNN(Convolutional Neural Network) to extract the features. After the features extracted, we put those features to classify. Detection module include three submouldes, review input submodule, model calculation submodule and review output submodel. Users input the reviews that they need to detect. And then, spam review detection system will deal the input and calculate the result using the saving model. Finally, it would return the result to the users.This paper first export requirements analysis and researching the key issues, and then introduce the designing and implementing of the submodule. It is applied testing for each module, and the results shows the designing and implementing meet the requirements. Finally, it states a summary of the whole paper.