User's Review Habits Enhanced Hierarchical Neural Network for Document-Level Sentiment Classification

Document-level sentiment classification is dedicated to predicting the sentiment polarity of document-level reviews posted by users about products and services. Many methods use neural networks have achieved very successful results on sentiment classification tasks. These methods usually focus on mining useful information from the text of the review documents. However, they ignore the importance of users' review habits. The reviews posted by the same user when commenting on different products contain similar review habits, and reviews that contain highly similar review habits often have similar sentiment ratings. In this paper, we propose a novel sentiment classification algorithm that utilizes user's review habits to enhance hierarchical neural networks, namely as HUSN. Firstly, we divide the reviews in the training set according to the users. All the reviews of each user are aggregated together and called the historical reviews of this user. Secondly, the target review in the test set and its multiple historical reviews in the training set are sent to the Long Short-Term Memory based hierarchical neural network to obtain the corresponding review document representations containing the user's review habits. Finally, we calculate the similarities between the target review document representation and multiple historical review document representations. The higher the similarity, the closer the review habits of different reviews from the same user, and the closer the corresponding sentiment ratings. Experimental results show that the similarities between the review habits of different reviews from the same user can further improve the performance of document-level sentiment classification. The HUSN algorithm performs better than all baseline methods on three publicly available document-level review datasets.