Sentiment lexicon creation from lexical resources

Today's business information systems face the challenge of analyzing sentiment in massive data sets for supporting, e.g., reputation management. Many approaches rely on lexical resources containing words and their associated sentiment. We perform a corpus-based evaluation of several automated methods for creating such lexicons, exploiting vast lexical resources. We consider propagating the sentiment of a seed set of words through semantic relations or through PageRank-based similarities. We also consider a machine learning approach using an ensemble of classifiers. The latter approach turns out to outperform the others. However, PageRank-based propagation appears to yield a more robust sentiment classifier.

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