DNN-driven Gradual Machine Learning for Aspect-Term Sentiment Analysis

Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML), which begins with some automatically labeled easy instances, and then gradually labels more challenging instances by iterative factor graph inference without manual intervention. As a non-i.i.d learning paradigm, GML leverages shared features between labeled and unlabeled instances for knowledge conveyance. However, the existing GML solution extracts sentiment features based on pre-specified lexicons, which are usually inaccurate and incomplete and thus lead to inadequate knowledge conveyance. In this paper, we propose a Deep Neural Network (DNN) driven GML approach for ATSA, which exploits the power of DNN in feature representation for gradual learning. It first uses an unsupervised neural network to cluster the automatically extracted features by their sentiment orientation. Then, it models the clustered features as factors to enable implicit knowledge conveyance for gradual inference in a factor graph. To leverage labeled training data, we also present a hybrid solution that fulfills gradual learning by fusing the influence of supervised DNN predictions and implicit knowledge conveyance in a unified factor graph. Finally, we empirically evaluate the performance of the proposed approach on real benchmark data. Our extensive experiments have shown that the proposed approach consistently achieves the state-of-the-art performance across all the test datasets in both unsupervised and supervised settings and the improvement margins are considerable.