Latent aspect mining via exploring sparsity and intrinsic information
Refereed conference paper presented and published in conference proceedings


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AbstractWe investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the domain, and predict the user's ratings on each aspect for each review. Another goal is to detect key terms for each aspect. Existing works on predicting aspect ratings fail to handle the aspect sparsity problem in the review texts leading to unreliable prediction. We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner. By considering the user and item side information of review texts, we introduce two latent variables, namely, user intrinsic aspect interest and item intrinsic aspect quality facilitating better modeling of aspect generation leading to improvement on the accuracy and reliability of predicted aspect ratings. Furthermore, we provide an analytical investigation on the Maximum A Posterior (MAP) optimization problem used in our proposed model and develop a new block coordinate gradient descent algorithm to efficiently solve the optimization with closed-form updating formulas. We also study its convergence analysis. Experimental results on the two real-world product review corpora demonstrate that our proposed model outperforms existing state-of-the-art models.
All Author(s) ListXu Y., Lin T., Lam W., Zhou Z., Cheng H., So A.M.-C.
Name of Conference23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Start Date of Conference03/11/2014
End Date of Conference07/11/2014
Place of ConferenceShanghai
Country/Region of ConferenceChina
Detailed descriptionorganized by ACM, SIGIR, CCF, Fudan University,
Year2014
Month1
Day1
Pages879 - 888
ISBN9781450325981
LanguagesEnglish-United Kingdom
KeywordsAspect mining, Sparse coding, Topic model

Last updated on 2021-03-03 at 23:48