Abstract
With the increasing popularity of aspect-level sentiment analysis, where sentiment is attributed to the actual aspects, or features, on which it is uttered, much attention is given to the problem of detecting these features. While most aspects appear as literal words, some are instead implied by the choice of words. With research in aspect detection advancing, we shift our focus to the less researched group of implicit features. By leveraging the co-occurrence between a set of known implicit features and notional words, we are able to predict the implicit feature based on the choice of words in a sentence. Using two different types of consumer reviews (product reviews and restaurant reviews), an F1-measure of 38% and 64% is obtained on these data sets, respectively.
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Schouten, K., Frasincar, F. (2014). Implicit Feature Extraction for Sentiment Analysis in Consumer Reviews. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_31
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DOI: https://doi.org/10.1007/978-3-319-07983-7_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07982-0
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