Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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Journal of Artificial Intelligence Research
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This paper is focused on one aspect of SO-PMI, an unsupervised approach to sentiment vocabulary acquisition proposed by Turney (Turney and Littman, 2003). The method, originally applied and evaluated for English, is often used in bootstrapping sentiment lexicons for European languages where no such resources typically exist. In general, SO-PMI values are computed from word co-occurrence frequencies in the neighbourhoods of two small sets of paradigm words. The goal of this work is to investigate how lexeme selection affects the quality of obtained sentiment estimations. This has been achieved by comparing ad hoc random lexeme selection with two alternative heuristics, based on clustering and SVD decomposition of a word co-occurrence matrix, demonstrating superiority of the latter methods. The work can be also interpreted as sensitivity analysis on SO-PMI with regard to paradigm word selection. The experiments were carried out for Polish.