NDLI: Dish comment summarization based on bilateral topic analysis

Content Provider	IEEE Xplore Digital Library
Author	Rong Zhang Zhenjie Zhang Xiaofeng He Aoying Zhou
Copyright Year	2015
Description	Author affiliation: Shanghai Key Lab. of Trustworthy Comput., East China Normal Univ., Shanghai, China (Rong Zhang; Xiaofeng He; Aoying Zhou) \|\| Adv. Digital Sci. Center, Illinois at Singapore Pte. Ltd., Singapore, Singapore (Zhenjie Zhang)
Abstract	With the prosperity of online services enabled by Web 2.0, huge amount of human generated commentary data are now available on the Internet, covering a wide range of domains on different products. Such comments contain valuable information for other customers, but are usually difficult to utilize due to the lack of common description structure, the complexity of opinion expression and fast growing data volume. Comment-based restaurant summarization is even more challenging than other types of products and services, as users' comments on restaurants are usually mixed with opinions on different dishes but attached with only one overall evaluation score on the whole experience with the restaurants. It is thus crucial to distinguish well-made dishes from other lousy dishes by mining the comment archive, in order to generate meaningful and useful summaries for other potential customers. This paper presents a novel approach to tackle the problem of restaurant comment summarization, with a core technique on the new bilateral topic analysis model on the commentary text data. In the bilateral topic model, the attributes discussed in the comments on the dishes and the user's evaluation on the attributes are considered as two independent dimensions in the latent space. Combined with new opinionated word extraction and clustering-based representation selection algorithms, our new analysis technique is effective to generate high-quality summary using representative snippets from the text comments. We evaluate our proposals on two real-world comment archives crawled from the most popular English and Chinese online restaurant review web sites, Yelp and Dianping. The experimental results verify the huge margin of advantage of our proposals on the summarization quality over baseline approaches in the literature.
Starting Page	483
Ending Page	494
File Size	413099
Page Count	12
File Format	PDF
ISBN	9781479979646
DOI	10.1109/ICDE.2015.7113308
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-13
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Hidden Markov models Analytical models Algorithm design and analysis Data mining Proposals Vocabulary
Content Type	Text
Resource Type	Article

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