Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A framework of feature selection methods for text categorization
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Document-level sentiment classification: An empirical comparison between SVM and ANN
Expert Systems with Applications: An International Journal
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Word relation features, which encode relation information between words, are supposed to be effective features for sentiment classification. However, the use of word relation features suffers from two issues. One is the sparse-data problem and the lack of generalization performance; the other is the limitation of using word relations as additional features to unigrams. To address the two issues, we propose a generalized word relation feature extraction method and an ensemble model to efficiently integrate unigrams and different type of word relation features. Furthermore, aimed at reducing the computation complexity, we propose two fast feature selection methods that are specially designed for word relation features. A range of experiments are conducted to evaluate the effectiveness and efficiency of our approaches.