Abstract
This paper presents a feature-driven opinion summarization method for customer reviews on the web based on identifying general features (characteristics) describing any product, product specific features and feature attributes (adjectives grading the characteristics). Feature attributes are assigned a polarity using on the one hand a previously annotated corpus and on the other hand by applying Support Vector Machines Sequential Minimal Optimization[1] machine learning with the Normalized Google Distance[2]. Reviews are statistically summarized around product features using the polarity of the feature attributes they are described by. © 2008 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Balahur, A., & Montoyo, A. (2008). Multilingual feature-driven opinion extraction and summarization from customer reviews. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5039 LNCS, pp. 345–346). https://doi.org/10.1007/978-3-540-69858-6_39
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
