An algorithm for suffix stripping
Readings in information retrieval
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
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
A holistic lexicon-based approach to opinion mining
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
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
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
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
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
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
The FLDA model for aspect-based opinion mining: addressing the cold start problem
Proceedings of the 22nd international conference on World Wide Web
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Aspect-based opinion mining, which aims to extract aspects and their corresponding ratings from customers reviews, provides very useful information for customers to make purchase decisions. In the past few years several probabilistic graphical models have been proposed to address this problem, most of them based on Latent Dirichlet Allocation (LDA). While these models have a lot in common, there are some characteristics that distinguish them from each other. These fundamental differences correspond to major decisions that have been made in the design of the LDA models. While research papers typically claim that a new model outperforms the existing ones, there is normally no "one-size-fits-all" model. In this paper, we present a set of design guidelines for aspect-based opinion mining by discussing a series of increasingly sophisticated LDA models. We argue that these models represent the essence of the major published methods and allow us to distinguish the impact of various design decisions. We conduct extensive experiments on a very large real life dataset from Epinions.com (500K reviews) and compare the performance of different models in terms of the likelihood of the held-out test set and in terms of the accuracy of aspect identification and rating prediction.