Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth 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
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
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Extracting key-substring-group features for text classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A Comparative Study of Methods for Transductive Transfer Learning
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
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An open problem in machine learning-based sentiment classification is how to extract complex features that outperform simple features; figuring out which types of features are most valuable is another Most of the studies focus primarily on character or word Ngrams features, but substring-group features have never been considered in sentiment classification area before In this study, the substring-group features are extracted and selected for sentiment classification by means of transductive learning-based algorithm To demonstrate generality, experiments have been conducted on three open datasets in three different languages: Chinese, English and Spanish The experimental results show that the proposed algorithm's performance is usually superior to the best performance in related work, and the proposed feature subsumption algorithm for sentiment classification is multilingual Compared to the inductive learning-based algorithm, the experimental results also illustrate that the transductive learning-based algorithm can significantly improve the performance of sentiment classification As for term weighting, the experiments show that the “tfidf-c” outperforms all other term weighting approaches in the proposed algorithm.