Relevance weighting of search terms
Document retrieval systems
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Document classification by machine: theory and practice
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
HLT '01 Proceedings of the first international conference on Human language technology research
Parametric models of linguistic count data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A hierarchical classifier applied to multi-way sentiment detection
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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This paper addresses the problem of supervised sentiment detection using classifiers which are derived from word features. We argue that, while the literature has suggested the use of lexical features is inappropriate for sentiment detection, a careful and thorough evaluation reveals a less clear-cut state of affairs. We present results from five classifiers using word-based features on three tasks, and show that the variation between classifiers can often be as great as has been reported between different feature sets with a fixed classifier. We are thus led to conclude that classifier choice plays at least as important a role as feature choice, and that in many cases word-based classifiers perform well on the sentiment detection task.