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
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
A cross-collection mixture model for comparative text mining
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
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Opinion integration through semi-supervised topic modeling
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 Cross-Lingual/Cross-Cultural Differences in Concerns and Opinions in Blogs
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
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
Sentiment analysis of blogs by combining lexical knowledge with text classification
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
Product feature categorization with multilevel latent semantic association
Proceedings of the 18th ACM conference on Information and knowledge management
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
MOETA: a novel text-mining model for collecting and analysing competitive intelligence
International Journal of Advanced Media and Communication
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Opinion mining focuses on extracting customers' opinions from the reviews and predicting their sentiment orientation. Reviewers usually praise a product in some aspects and bemoan it in other aspects. With the business globalization, it is very important for enterprises to extract the opinions toward different aspects and find out cross-lingual/cross-culture difference in opinions. Cross-lingual opinion mining is a very challenging task as amounts of opinions are written in different languages, and not well structured. Since people usually use different words to describe the same aspect in the reviews, product-feature (PF) categorization becomes very critical in cross-lingual opinion mining. Manual cross-lingual PF categorization is time consuming, and practically infeasible for the massive amount of data written in different languages. In order to effectively find out cross-lingual difference in opinions, we present an aspect-oriented opinion mining method with Cross-lingual Latent Semantic Association (CLaSA). We first construct CLaSA model to learn the cross-lingual latent semantic association among all the PFs from multi-dimension semantic clues in the review corpus. Then we employ CLaSA model to categorize all the multilingual PFs into semantic aspects, and summarize cross-lingual difference in opinions towards different aspects. Experimental results show that our method achieves better performance compared with the existing approaches. With CLaSA model, our text mining system OpinionIt can effectively discover cross-lingual difference in opinions.