A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Direction-based text interpretation as an information access refinement
Text-based intelligent systems
On the computation of point of view
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
PHOAKS: a system for sharing recommendations
Communications of the ACM
Virtual reviewers for collaborative exploration of movie reviews
Proceedings of the 5th international conference on Intelligent user interfaces
A vector space model for automatic indexing
Communications of the ACM
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Parsing with discontinuous constituents
ACL '85 Proceedings of the 23rd annual meeting on 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
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Smokey: automatic recognition of hostile messages
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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This paper explores the sentiment classification with Information Extraction (IE) approach. The IE approach here is required to detect the sentiment expressions on specific subject (person, product, company and so on) and then to evaluate the sentiment strength and/or the validation of them. Our method can be illustrated logically as: (1) From a given text, extract the sentiment expressions on the specific subjects and attach certain sentiment tag and weight to each of them; (2) Calculate the sentiment indicator for each sentiment genre by accumulating the weights of all the expression with the corresponding tag; (3) Given the indicators on different sentiment genres, use a classifier to predict the sentiment label of the given text. To extract expression robustly when encounter some complex linguistic phenomena (such as ellipsis, anaphora), a new parsing idea named super parsing is proposed. It enables some non-adjacent linguistic constituents to be merged to deduce a new one. As an incremental implementation of super parsing, a system named Approximate Text Analysis (ATA) is described in this paper. As for the classification task, two different classifiers are used: simple linear classifier (called SLC here) and SVM. The experiments show the reasonable performance of our approach.