Abstract
We study the effect of negation cues on semantic orientation prediction. State-of-the-art approaches to semantic orientation derivation are based on automatic classification. We analyze the use of negation cues as features for both supervised and unsupervised methods. We apply such methods on a collection of washing-machine reviews in Spanish. We compare the results of the two approaches and discuss the performance of each negation cue. We found that simple features performed similarly to using more resources.
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Notes
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http://sentiwordnet.isti.cnr.it/, lexical resource for opinion mining that assigns three sentiment scores: positivity, negativity and objectivity.
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The fourth author recognizes the support of the Instituto Politécnico Nacional, grants SIP 20152095 and SIP 20152100.
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Galicia-Haro, S.N., Palomino-Garibay, A., Gallegos-Acosta, J., Gelbukh, A. (2015). Analysis of Negation Cues for Semantic Orientation Classification of Reviews in Spanish. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_8
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