  Aspect level sentiment classification is a fine-grained sentiment analysis
task. To detect the sentiment towards a particular aspect in a sentence,
previous studies have developed various attention-based methods for generating
aspect-specific sentence representations. However, the attention may inherently
introduce noise and downgrade the performance. In this paper, we propose
constrained attention networks (CAN), a simple yet effective solution, to
regularize the attention for multi-aspect sentiment analysis, which alleviates
the drawback of the attention mechanism. Specifically, we introduce orthogonal
regularization on multiple aspects and sparse regularization on each single
aspect. Experimental results on two public datasets demonstrate the
effectiveness of our approach. We further extend our approach to multi-task
settings and outperform the state-of-the-art methods.
