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?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Natural Language Processing with Python
Natural Language Processing with Python
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
Incremental Boolean combination of classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
A Survey on Graphical Methods for Classification Predictive Performance Evaluation
IEEE Transactions on Knowledge and Data Engineering
A multi-classifier system for off-line signature verification based on dissimilarity representation
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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Although successfully employed to reduce error rates of difficult pattern recognition problems, multi-classifier systems (MCS) are not in widespread use in the field of Sentiment Analysis and Opinion Mining. The motivation of using a MCS stems from the fact that different classifiers usually make different errors on different samples. By using just the best classifier, it is possible to loose valuable information contained in the other sub optimal classifiers. In this work, we take advantage of unigrams, big rams and trig rams to design a multi-classifier system for Sentiment Analysis and Opinion Mining. Three different Naive Bayes classifiers are trained--each one with a specific set of features-- , and then combined in the ROC space by using the Iterative Boolean Combination (IBC) technique. IBC iteratively combines the ROC curves produced by different classifiers using all Boolean functions, and does not require prior assumption that the classifiers are statistically independent. An experimental study investigates the advantage of using the proposed MCS, over each individual classifier, in classifying Twitter messages as positive or negative. The Stanford University's Twitter database is employed for this task. As real-world application, the proposed MCS is used to identify the sentiment of electors regarding the main candidates for the 2012 United States Presidential Elections. Results indicate that the proposed MCS can provide useful information about people's opinions that are comparable to conventional opinion polls.