Fine granular aspect analysis using latent structural models
Date
2012
Authors
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Volume Title
Publisher
Association for Computational Linguistics
Abstract
In this paper, we present a structural learning model for joint sentiment classification and aspect analysis of text at various levels of granularity. Online reviews have become a major resource where users find opinions or comments on products or services they want to consume. Aspect level sentiment analysis may be useful for a more global picture of opinions on the product’s properties. The resulting model is able to predict the sentiment polarity of a document as well as to identify aspect-specific sentences. A machine-learning algorithm generalizes the Support Vector Machine (SVM) classifier.
Description
Meeting: 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea, 8-14 July 2012
item.page.type
Conference Paper
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Text
Keywords
SENTIMENT ANALYSIS, CUSTOMER REVIEWS, ARTIFICIAL INTELLIGENCE
Citation
Lei Fang, & Minlie Huang (2012). Fine Granular Aspect Analysis using Latent Structural Models. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 333-337.