The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd 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
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
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
Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
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
Expert Systems with Applications: An International Journal
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Nowadays, online word-of-mouth has turned to be a very important resource for electronic businesses. How to analyze user generated reviews and to classify them into different sentiment classes is gradually becoming a question that people pay close attention to. In this field, special challenges are associated with the mining of traveler reviews. At present, there is some research on sentiment analysis for English traveler generated reviews, but very few studies pay attention to sentiment analysis for traveler reviews in Chinese. China is the largest country in terms of the number of Internet users. Internet technologies are gradually playing more and more important roles for many industries including tourism industry. The lack of sentiment analysis methods will block the use of word-of-mouth for tourism industry in China. To solve the problem, this study conducts an exploring research on sentiment analysis to Chinese traveler reviews by support vector machine (SVM) algorithm. The experiment data of Chinese reviews for hotels are downloaded from www.ctrip.com, the largest online travel agency in China. Empirical results indicate that, comparing to prior studies on English reviews, SVM algorithm can gain a very well performance of sentiment classification for traveler reviews in Chinese.