  In this paper, we introduce a new framework called the sentiment-aspect
attribution module (SAAM). SAAM works on top of traditional neural networks and
is designed to address the problem of multi-aspect sentiment classification and
sentiment regression. The framework works by exploiting the correlations
between sentence-level embedding features and variations of document-level
aspect rating scores. We demonstrate several variations of our framework on top
of CNN and RNN based models. Experiments on a hotel review dataset and a beer
review dataset have shown SAAM can improve sentiment analysis performance over
corresponding base models. Moreover, because of the way our framework
intuitively combines sentence-level scores into document-level scores, it is
able to provide a deeper insight into data (e.g., semi-supervised sentence
aspect labeling). Hence, we end the paper with a detailed analysis that shows
the potential of our models for other applications such as sentiment snippet
extraction.
