C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
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
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
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Expert Systems with Applications: An International Journal
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Seeing several stars: a rating inference task for a document containing several evaluation criteria
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A comparison of sentiment analysis techniques: polarizing movie blogs
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
A study of information retrieval weighting schemes for sentiment analysis
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Sentiment classification of Internet restaurant reviews written in Cantonese
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Exploiting effective features for chinese sentiment classification
Expert Systems with Applications: An International Journal
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Expert Systems with Applications: An International Journal
Feature-based opinion mining and ranking
Journal of Computer and System Sciences
A document-level sentiment analysis approach using artificial neural network and sentiment lexicons
ACM SIGAPP Applied Computing Review
A boosted SVM based sentiment analysis approach for online opinionated text
Proceedings of the 2013 Research in Adaptive and Convergent Systems
A boosted SVM based ensemble classifier for sentiment analysis of online reviews
ACM SIGAPP Applied Computing Review
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Sentiment analysis is performed to extract opinion and subjectivity knowledge from user generated text content. This is contextually different from traditional topic based text classification since it involves classifying opinionated text according to the sentiment conveyed by it. Feature selection is a critical task in sentiment analysis and effectively selected representative features from subjective text can improve sentiment based classification. This paper explores the applicability of five commonly used feature selection methods in data mining research (DF, IG, GR, CHI and Relief-F) and seven machine learning based classification techniques (Naïve Bayes, Support Vector Machine, Maximum Entropy, Decision Tree, K-Nearest Neighbor, Winnow, Adaboost) for sentiment analysis on online movie reviews dataset. The paper demonstrates that feature selection does improve the performance of sentiment based classification, but it depends on the method adopted and the number of feature selected. The experimental results presented in this paper show that Gain Ratio gives the best performance for sentimental feature selection, and SVM performs better than other techniques for sentiment based classification.