Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Exploring in the weblog space by detecting informative and affective articles
Proceedings of the 16th international conference on World Wide Web
Expert Systems with Applications: An International Journal
Feature selection for text classification with Naïve Bayes
Expert Systems with Applications: An International Journal
Sentiment Classification with Support Vector Machines and Multiple Kernel Functions
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Sentiment classification of online Cantonese reviews by supervised machine learning approaches
International Journal of Web Engineering and Technology
Feature selection on Chinese text classification using character n-grams
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Sentiment classification of Internet restaurant reviews written in Cantonese
Expert Systems with Applications: An International Journal
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With the boost of online reviews, a large quantity of consumers' opinions on certain products and services are generated and spread over the internet, thus techniques of sentiment classification for online reviews rise in response to the requirement of retrieving valuable information. This paper is mainly focused on improving sentiment classification of Chinese online reviews through analysing and improving each step in supervised machine learning. At first, adjectives, adverbs, and verbs are selected as the initial text features. Then, three statistic methods (DF, IG and CHI) are utilised to extract features. At last, a Boolean method is applied to set weight to features and a support vector machine (SVM) is employed as the classifier. Several comparative experiments have been conducted on reviews of two domains: mobile phone (product) reviews and hotel (service) reviews. The experimental results indicate that part of speech (POS), the number of features, evaluation domain, feature extraction algorithm and kernel function of SVM have great influences on sentiment classification, while the number of training corpora has a little impact. In addition, further improvements of DF IG and CHI have been made, which demonstrate the theoretical significance and the practical value of this research.