Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Natural Language Engineering
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Using encyclopedic knowledge for automatic topic identification
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Carrot2: design of a flexible and efficient web information retrieval framework
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Exploratory search on social media
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Text mining of massive Social Media postings presents interesting challenges for NLP applications due to sparse interpretation contexts, grammatical and orthographical variability as well as its very fragmentary nature. No single methodological approach can be expected to work across such diverse typologies as twitter micro-blogging, customer reviews, carefully edited blogs, etc. In this paper we present a modular and scalable framework to Social Media Opinion Mining that combines stochastic and symbolic techniques to structure a semantic space to exploit and interpret efficiently. We describe the use of this framework for the discovery and clustering of opinion targets and topics in user-generated comments for the Telecom and Automotive domains.