Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains

This work investigates the use of Semi-Supervised Learning in opinion detection both in sparse data situations and for domain adaptation, and shows that co-training reaches the best results in an in-domain setting with small labeled data sets.

Abstract

We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in sparse data situations and for domain adaptation. We show that co-training reaches the best results in an in-domain setting with small labeled data sets, with a maximum absolute gain of 33.5%. For domain transfer, we show that self-training gains an absolute improvement in labeling accuracy for blog data of 16 % over the supervised approach with target domain training data. 1