Word association norms, mutual information, and lexicography
Computational Linguistics
WordNet: a lexical database for English
Communications of the ACM
Information Retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
AcroDef: a quality measure for discriminating expansions of ambiguous acronyms
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Stakeholder detection for online debates
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Towards an automatic characterization of criteria
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Comparing different methods for opinion mining in newspaper articles
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Learning analytics for learning blogospheres
ICWL'12 Proceedings of the 11th international conference on Advances in Web-Based Learning
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The growing popularity of Web 2.0 provides with increasing numbers of documents expressing opinions on different topics. Recently, new research approaches have been defined in order to automatically extract such opinions from the Internet. They usually consider opinions to be expressed through adjectives, and make extensive use of either general dictionaries or experts to provide the relevant adjectives. Unfortunately, these approaches suffer from the following drawback: in a specific domain, a given adjective may either not exist or have a different meaning from another domain. In this paper, we propose a new approach focusing on two steps. First, we automatically extract a learning dataset for a specific domain from the Internet. Secondly, from this learning set we extract the set of positive and negative adjectives relevant to the domain. The usefulness of our approach was demonstrated by experiments performed on real data.