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
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
OpinionMiner: a novel machine learning system for web opinion mining and extraction
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Product feature categorization with multilevel latent semantic association
Proceedings of the 18th ACM conference on Information and knowledge management
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Hierarchical sequential learning for extracting opinions and their attributes
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Opinion digger: an unsupervised opinion miner from unstructured product reviews
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Structure-aware review mining and summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Normalizing web product attributes and discovering domain ontology with minimal effort
Proceedings of the fourth ACM international conference on Web search and data mining
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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
Latent aspect rating analysis without aspect keyword supervision
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Aspect extraction through semi-supervised modeling
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
Local business ambience characterization through mobile audio sensing
Proceedings of the 23rd international conference on World wide web
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Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.