Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
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
Fast discovery of unexpected patterns in data, relative to a Bayesian network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Sequential patterns for text categorization
Intelligent Data Analysis
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
ICDM '06 Proceedings of the Sixth International Conference on 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
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Using wiktionary for computing semantic relatedness
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
Fast categorization of web documents represented by graphs
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Automatic hierarchical classification of structured deep web databases
WISE'06 Proceedings of the 7th international conference on Web Information Systems
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Sentiment classification in text documents is an active data mining research topic in opinion retrieval and analysis. Different from previous studies concentrating on the development of effective classifiers, in this paper, we focus on the extraction and validation of unexpected sentences issued in sentiment classification. In this paper, we propose a general framework for determining unexpected sentences. The relevance of the extracted unexpected sentences is assessed in the context of text classification. In the experiments, we present the extraction of unexpected sentences for sentiment classification within the proposed framework, and then evaluate the influence of unexpected sentences on the quality of classification tasks. The experimental results show the effectiveness and usefulness of our proposed approach.