Automatically Generating Annotator Rationales to Improve Sentiment Classification

One of the central challenges in sentimentbased 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.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Ainur Yessenalina, Yejin Choi, Claire Cardie
• Keywords: ACL 2010, Annotator Rationales, Computational Linguistics, Document-level Sentiment Classification, Zaidan Et Al