Automatically Generating Annotator Rationales to Improve Sentiment Classification

Ainur Yessenalina, Yejin Choi, Claire Cardie

Abstract: One of the central challenges in sentiment-based text categorization is that not every portion of a document is equally informative for inferring the overall sentiment of the document. Previous research has shown that enriching the sentiment labels with human annotators' "rationales" can produce substantial improvements in categorization performance (Zaidan et al., 2007). We explore methods to automatically generate annotator rationales for document-level sentiment classification. Rather unexpectedly, we find the automatically generated rationales just as helpful as human rationales.