Direction-based text interpretation as an information access refinement
Text-based intelligent systems
On the computation of point of view
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
The sentimental factor: improving review classification via human-provided information
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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
Leveraging Sentiment Analysis for Topic Detection
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The impact of time on the accuracy of sentiment classifiers created from a web log corpus
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Scary films good, scary flights bad: topic driven feature selection for classification of sentiment
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Leveraging sentiment analysis for topic detection
Web Intelligence and Agent Systems
Employing personal/impersonal views in supervised and semi-supervised sentiment classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A generate-and-test method of detecting negative-sentiment sentences
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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As the number of web logs dramatically grows, readers are turning to them as an important source of information. Automatic techniques that identify the political sentiment of web log posts will help bloggers categorize and filter this exploding information source. In this paper we illustrate the effectiveness of supervised learning for sentiment classification on web log posts. We show that a Naïve Bayes classifier coupled with a forward feature selection technique can on average correctly predict a posting's sentiment 89.77% of the time with a standard deviation of 3.01. It significantly outperforms Support Vector Machines at the 95% confidence level with a confidence interval of [1.5, 2.7]. The feature selection technique provides on average an 11.84% and a 12.18% increase for Naïve Bayes and Support Vector Machines results respectively. Previous sentiment classification research achieved an 81% accuracy using Naïve Bayes and 82.9% using SVMs on a movie domain corpus.