Latent Aspect Rating Analysis on Chinese Reviews: A Local LDA Based Approach
Date Issued
2014
Date
2014
Author(s)
Chang, Kai-Ti
Abstract
As the growth of web technology, it’s an important task to mine the detailed information in the online reviews. Most reviewers only rating the entity with overall rating; however, it’s not enough for users to learn more from the reviews. As a result, there is a new problem called Latent Aspect Rating Analysis in text mining which analyzes latent aspect and latent aspect weight simultaneously. In this research, we apply the LARA on the Chinese reviews. We use the Local LDA(unsupervised learning) and LRR model to analyze the online reviews. In the first stage, we use the Local LDA method on the review contexts to conduct the aspect segmentation after preprocessing. After the aspect segmentation, we can get the aspects and aspect representative words. In the second stage, we use the LRR model to infer the latent aspect rating and latent aspect weight. Our experiment uses the Ctrip and TripAdvisor online reviews as the dataset. The results demonstrate the Local LDA + LRR method has some advantage on Chinese LARA problems.
Subjects
text mining
Latent Aspect Rating Analysis
Latent Dirichlet Allocation
Latent Rating Regression Model
sentiment analysis
opinion mining
review mining
Type
thesis
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