ELS

Abstract

We introduce ELS, a new method for entity-level sentiment classification using sequence modeling by Conditional Random Fields (CRF). The CRF is trained to identify the sentiment of each word in a document, which is then used to determine the sentiment for the entity, based on where it appears in the text. Due to its sequential nature, the CRF classifier performs better than the common bag-of-words approaches, especially when we target the local sentiment in small parts of a larger document. Identifying the sentiment about a specific entity, mentioned in a blog post or a larger product review, is a special case of such local sentiment classification. Furthermore, the proposed approach performs well even in short pieces of text, where bag-of-words approaches usually fail, due to the sparseness of the resulting feature vector. We have implemented and tested the proposed method on a publicly available benchmark corpus of short product reviews in English. The results that we present in this paper improve significantly upon published results on the same data, thus confirming our intuition about the approach.