By Topic

Sentiment classification for Chinese reviews based on key substring features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zhongwu Zhai ; State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology, CS&T Department, Tsinghua University, Beijing 100084, China P.R. ; Hua Xu ; Jun Li ; Peifa Jia

One of the most widely-studied sub-problems of opinion mining is sentiment classification, which classifies evaluative texts as positive or negative to help people automatically identify the viewpoints underlying the online user-generated information. Most of the existing methods for sentiment classification ignore word sequence and unlabeled test documents' structural information. This paper proposes a transductive learning based algorithm considering both of these two types of information. The proposed algorithm is implemented by firstly selecting key substrings in the suffix tree constructed from the strings in training and unlabeled test documents and then converting each original text document to a bag of numbers of the key substrings. Finally, SVM is employed to classify the converted documents. Experiments on the open dataset (16,000 Chinese reviews) demonstrate promising performance of the proposed algorithm, the accuracy being over 93.15%, which is much better than the performance of the existing sentiment classification methods, such as n-gram features based classification algorithms. Experimental results also show that ldquotfidf-crdquo performs much better than other term weighting approaches in sentiment classification for large text corpus. In particular, the reasons behind the proposed algorithm's outstanding performance are further studied and analyzed in this paper. Moreover, the proposed algorithm can avoid the messy and rather artificial problem of defining word boundaries in Chinese language.

Published in:

Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on

Date of Conference:

24-27 Sept. 2009