Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively 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. In GML, shared features serve as the medium for knowledge conveyance between unlabeled and labeled instances. However, the existing GML solution extracts features based on lexicons, which may not be accurate nor complete and thus lead to insufficient knowledge conveyance.

In this paper, we propose a Deep Neural Network (DNN) driven GML approach for ATSA, which can effectively leverage the strength of DNN in feature representation. It first uses an unsupervised neural network to cluster the automatically extracted features by their sentiment polarities. Then, it models the clustered features as factors to enable implicit knowledge conveyance for gradual inference in a factor graph. Furthermore, provided with labeled training data, we present a hybrid solution that similarly enables gradual learning by fusing the influence of DNN predictions and implicit knowledge conveyance. Finally, we have empirically evaluated 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.

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

Findings Papers - Sentiment Analysis, Stylistic Analysis, and Argument Mining

technical paper

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

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