Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning

Sentiment relevance (SR) aims at identifying content that does not contribute to sentiment analysis. Previously, automatic SR classification has been studied in a limited scope, using a single domain and feature augmentation techniques that require large hand-crafted databases. In this paper, we present experiments on SR classification with automatically learned feature representations on multiple domains. We show that a combination of transfer learning and in-task supervision using features learned unsupervisedly by the stacked denoising autoencoder significantly outperforms a bag-of-words baseline for in-domain and cross-domain classification.