Building Subjectivity Lexicon(s) from Scratch for Essay Data

While there are a number of subjectivity lexicons available for research purposes, none can be used commercially. We describe the process of constructing subjectivity lexicon(s) for recognizing sentiment polarity in essays written by test-takers, to be used within a commercial essay-scoring system. We discuss ways of expanding a manually-built seed lexicon using dictionary-based, distributional indomain and out-of-domain information, as well as using Amazon Mechanical Turk to help “clean up” the expansions. We show the feasibility of constructing a family of subjectivity lexicons from scratch using a combination of methods to attain competitive performance with state-of-art research-only lexicons. Furthermore, this is the first use, to our knowledge, of a paraphrase generation system for expanding a subjectivity lexicon.

• Type: Journal
• Year: 2012
• Where: CICLING
• Authors: Beata Beigman Klebanov, Jill Burstein, Nitin Madnani, Adam Faulkner, Joel R. Tetreault
• Keywords: Amazon Mechanical Turk, Art Research, CICLING 2012, Lexicons, Natural Language Processing