Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
Sentiment summarization: evaluating and learning user preferences
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
IEEE Intelligent Systems
Exploring domain-specific term weight in archived question search
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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In this paper, domain-knowledge extraction and aspect- opinion extraction are proposed in order to generate a summary from the relevant product and service review. In order to extract the word corresponding to aspect and opinion, we extract the domain-salient word and collocation information by applying statistical techniques from the bulk of the text, and construct the clue words through manual filtering. In domain knowledge extraction, in order to extract useful information, domain-salient words which occur more significantly in a given domain rather than in a public domain article are automatically extracted by using the statistical techniques. As well, collocation information has the association with high frequency words. In recognition of aspect-opinion association, words corresponding to aspects and opinions in a sentence are checked by using information of clue words, and the polarity of the sentence is determined by performing pattern-based modality analysis. Through checking the binary association based on the frequency of co-occurrence, a pair of aspect and opinion is extracted, our system can automatically acquire the scores for a review target based of the degree of positive/negative.