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
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
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
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Adding redundant features for CRFs-based sentence sentiment classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Document sentiment classification by exploring description model of topical terms
Computer Speech and Language
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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Sentiment analysis is the process of analyzing and classifying the rewires contents about a product, event, and place etc into positive, negative or neutral opinion. In this paper; we propose a sentence level machine learning approach for sentiment classification of online reviews. The proposed method extracts the subjective sentences from the reviews and label each sentence either positive or negative based on its word level feature using naïve Naïve Bayesian (NB) classifier. The labeled sentences create an annotated set of sentences called as BOS (Bag-of-Sentences). We train Support Vector machine (SVM) classifier on the BOS for sentences polarity classification. The contextual information in each sentence structure is taken into consideration to calculate the semantic orientation. The effectiveness of the proposed method is evaluated thought simulation. Results show that our machine learning based proposed method on average achieves accuracy of 81% and 83% with some contextual information. This method improves the sentiment classification polarity on sentence level unlike the word level lexical feature based work, by focus on sentences, this also concentrate on contextual information.