As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, aut... more As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, automated ways of analyzing sentiment in such data are becoming more and more urgent. In this paper, we provide a classification scheme for existing approaches to document sentiment analysis. As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally investigate the impact of taking into account negation when analyzing sentiment. To this end, we utilize a basic sentiment analysis framework -consisting of a wordbank creation part and a document scoring part -taking into account negation. Our experimental results show that by accounting for negation, precision on human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%.
Lecture Notes in Business Information Processing, 2011
Today's business information systems face the challenge of analyzing sentiment in massive data se... more 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.
ABSTRACT A key element for decision makers to track is their stakeholders' sentiment. Rec... more ABSTRACT A key element for decision makers to track is their stakeholders' sentiment. Recent developments show a tendency of including various aspects other than word frequencies in automated sentiment analysis approaches. One of these aspects is negation, which can be accounted for in various ways. We compare several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a significant increase in overall sentiment classification accuracy and macro-level F1 of 5.5% and 6.2%, respectively, compared to not accounting for negation. Additionally optimizing sentiment modification of negated words to a value of -1.27 rather than -1 yields a significant 7.1% increase in accuracy and a significant 8.0% increase in macro-level F1.
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Papers by Bas Heerschop