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Conference proceeding
Sentiment classification based on supervised latent n-gram analysis
Proceedings of the 20th ACM international conference on information and knowledge management, pp 375-382
24 Oct 2011
Abstract
In this paper, we propose an efficient embedding for modeling higher-order (n-gram) phrases that projects the n-grams to low-dimensional latent semantic space, where a classification function can be defined. We utilize a deep neural network to build a unified discriminative framework that allows for estimating the parameters of the latent space as well as the classification function with a bias for the target classification task at hand. We apply the framework to large-scale sentimental classification task. We present comparative evaluation of the proposed method on two (large) benchmark data sets for online product reviews. The proposed method achieves superior performance in comparison to the state of the art.
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106 citations in Scopus
Details
- Title
- Sentiment classification based on supervised latent n-gram analysis
- Creators
- Dmitriy Bespalov - Drexel UniversityBing Bai - Princeton UniversityYanjun Qi - Princeton UniversityAli Shokoufandeh - Drexel University
- Publication Details
- Proceedings of the 20th ACM international conference on information and knowledge management, pp 375-382
- Series
- CIKM '11
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-83055181771
- Other Identifier
- 991019173657904721