Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
This work provides an incremental improvement for researchers and practitioners working on multi-aspect sentiment analysis.
This paper introduces the sentiment-aspect attribution module (SAAM) to improve multi-aspect sentiment classification and regression by exploiting correlations between sentence-level embeddings and document-level aspect ratings. Experiments on hotel and beer review datasets show SAAM enhances sentiment analysis performance over base CNN and RNN models.
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.