Sentiment information extraction of comparative sentences based on CRF model

Abstract

Comparative information mining is an important research topic in the sentiment analysis community. A comparative sentence expresses at least one similarity or difference relation between two objects. For example, the comparative sentence “The space of car A is bigger than that of car B and car C” expresses two comparative relations and. This paper introduces conditional random fields model to extract Chinese comparative information and focuses on the task of element extraction from comparative sentences. We use the conditional random fields model to combine diverse lexical, syntactic and semantic features derived from the texts. Experiments show that the proposed method is competitive and domain-independent, with promising results.