We examine the viability of building large polarity lexicons semi-automatically from the web. We begin by describing a graph propagation framework inspired by previous work on constructing polarity lexicons from lexical graphs (Kim and Hovy, 2004; Hu and Liu, 2004; Esuli and Sabastiani, 2009; BlairGoldensohn et al., 2008; Rao and Ravichandran, 2009 ). We then apply this technique to build an English lexicon that is significantly larger than those previously studied. Crucially, this web-derived lexicon does not require WordNet, part-of-speech taggers, or other language-dependent resources typical of sentiment analysis systems. As a result, the lexicon is not limited to specific word classes -e.g., adjectives that occur in WordNetand in fact contains slang, misspellings, multiword expressions, etc. We evaluate a lexicon derived from English documents, both qualitatively and quantitatively, and show that it provides superior performance to previously studied lexicons, including one derived from WordNet.
REF use graph propagation algorithms for constructing a web-scale polarity lexicon for sentiment analysis.
The viability of web-derived polarity lexicons
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science