Word association norms, mutual information, and lexicography
Computational Linguistics
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Methods for the qualitative evaluation of lexical association measures
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A nonparametric method for extraction of candidate phrasal terms
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
An unsupervised framework for extracting and normalizing product attributes from multiple web sites
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
OpinionIt: a text mining system for cross-lingual opinion analysis
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Opinion digger: an unsupervised opinion miner from unstructured product reviews
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting web reviews for generating customer service surveys
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Grouping product features using semi-supervised learning with soft-constraints
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Clustering product features for opinion mining
Proceedings of the fourth ACM international conference on Web search and data mining
Normalizing web product attributes and discovering domain ontology with minimal effort
Proceedings of the fourth ACM international conference on Web search and data mining
EagleEye: entity-centric business intelligence for smarter decisions
IBM Journal of Research and Development
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Constrained LDA for grouping product features in opinion mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Semantic dependent word pairs generative model for fine-grained product feature mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
What Makes a Phone a Business Phone - Querying Concepts in Product Data
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Domain customization for aspect-oriented opinion analysis with multi-level latent sentiment clues
Proceedings of the 20th ACM international conference on Information and knowledge management
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Structuring e-commerce inventory
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
On the design of LDA models for aspect-based opinion mining
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the ACM International Conference on Computing Frontiers
The FLDA model for aspect-based opinion mining: addressing the cold start problem
Proceedings of the 22nd international conference on World Wide Web
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In recent years, the number of freely available online reviews is increasing at a high speed. Aspect-based opinion mining technique has been employed to find out reviewers' opinions toward different product aspects. Such finer-grained opinion mining is valuable for the potential customers to make their purchase decisions. Product-feature extraction and categorization is very important for better mining aspect-oriented opinions. Since people usually use different words to describe the same aspect in the reviews, product-feature extraction and categorization becomes more challenging. Manually product-feature extraction and categorization is tedious and time consuming, and practically infeasible for the massive amount of products. In this paper, we propose an unsupervised product-feature categorization method with multilevel latent semantic association. After extracting product-features from the semi-structured reviews, we construct the first latent semantic association (LaSA) model to group words into a set of concepts according to their virtual context documents. It generates the latent semantic structure for each product-feature. The second LaSA model is constructed to categorize the product-features according to their latent semantic structures and context snippets in the reviews. Experimental results demonstrate that our method achieves better performance compared with the existing approaches. Moreover, the proposed method is language- and domain-independent.