Paper: Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning

ACL ID E14-4039
Title Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Sentiment relevance (SR) aims at identify- ing content that does not contribute to sen- timent 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 repre- sentations 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.