In addition to these Context features and Context-free features, we also developed a feature that represents a mix of these two types, that was proven essential to our performance, and relied on an extension of the Sequential Pattern Matching (SPM) algorithm (Srikant and Agrawal, 1996).
for this Se-quentialPatternMatch feature, each sentence token was encoded as a tuple describing several attributes for that token -e.g., the token's text, the token's POS tag, and various binary indicators, indicating if the token is a sentiment word, if it is mentioned in the given Topic, if it is included in an automatically learned lexicon of "claim words", and if it is identified by a NER utility (Finkel et al., 2005).