Semi-supervised Learning for Opinion Detection

Published 1 August 2010

Ning Yu, Sandra Kübler

DOI10.1109/wi-iat.2010.263

Citations11

TL;DR

Preliminary results show that SSL performance is very close to a supervised system trained on the full data set and improves performance on out-of-domain data.

Abstract

Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the sub-document level, are often limited. This paper describes the application of Semi-Supervised Learning (SSL) to automatically produce more labeled data and explores the potential of SSL to improve transfer of labeled data to new domains. Preliminary results show that SSL performance is very close to a supervised system trained on the full data set and improves performance on out-of-domain data.

Keywords

Computer Science

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