  The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA)
were built on a variety of deep neural networks (DNN), whose efficacy depends
on large amounts of accurately labeled training data. Unfortunately,
high-quality labeled training data usually require expensive manual work, and
may thus not be readily available in real scenarios. In this paper, we propose
a novel solution for ALSA based on the recently proposed paradigm of gradual
machine learning, which can enable effective machine labeling without the
requirement for manual labeling effort. It begins with some easy instances in
an ALSA task, which can be automatically labeled by the machine with high
accuracy, and then gradually labels the more challenging instances by iterative
factor graph inference. In the process of gradual machine learning, the hard
instances are gradually labeled in small stages based on the estimated
evidential certainty provided by the labeled easier instances. Our extensive
experiments on the benchmark datasets have shown that the performance of the
proposed solution is considerably better than its unsupervised alternatives,
and also highly competitive compared to the state-of-the-art supervised DNN
techniques.
