Extracting Domain-Dependent Semantic Orientations of Latent Variables for Sentiment Classification

Authors:
Yeha Lee;Jungi Kim;Jong-Hyeok Lee
Affiliations:
Division of Electrical and Computer Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea 790---784;Division of Electrical and Computer Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea 790---784;Division of Electrical and Computer Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea 790---784
Venue:
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Year:
2009

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Abstract

Sentiment analysis of weblogs is a challenging problem. Most previous work utilized semantic orientations of words or phrases to classify sentiments of weblogs. The problem with this approach is that semantic orientations of words or phrases are investigated without considering the domain of weblogs. Weblogs contain the author's various opinions about multifaceted topics. Therefore, we have to treat a semantic orientation domain-dependently. In this paper, we present an unsupervised learning model based on aspect model to classify sentiments of weblogs. Our model utilizes domain-dependent semantic orientations of latent variables instead of words or phrases, and uses them to classify sentiments of weblogs. Experiments on several domains confirm that our model assigns domain-dependent semantic orientations to latent variables correctly, and classifies sentiments of weblogs effectively.