Multilingual opinion holder and target extraction using knowledge-poor techniques

Abstract

We describe an approach to multilingual sentiment analysis, in particular opinion holder and opinion target extraction, which requires no annotated data and minimal language-specific input. The approach is based on unsupervised, knowledge-poor techniques which facilitate adaptation to new languages and domains. The system's results are comparable to those of supervised, language-specific systems previously applied to the NTCIR-7 MOAT evaluation data.