Mining personal experiences and opinions from Web documents

Abstract

This paper proposes a new UGC-oriented language technology application, which we call experience mining. Experience mining aims at automatically collecting instances of personal experiences as well as opinions from vast amounts of user generated content (UGC) such as weblog and forum posts and storing them in an experience database with semantically rich indices. After discussing the technical issues relating to this new task, we focus on the central problem of factuality analysis, formulate a task definition, and propose a machine learning-based solution. Our empirical evaluation indicates that our factuality analysis defintion is sufficiently well-defined to achieve a high inter-annotator agreement and our Factorial CRF-based model considerably outperforms the baseline. We also present an application system, which currently stores over 50M experience instances extracted from 150M Japanese blog posts with semantic indices and serves an experience search engine for unrestricted users and report on our empirical evaluation of the system's accuracy.