Topic-Independent Chinese Sentiment Identification from Online News-Reference-Cited by-同舟云学术

Topic-Independent Chinese Sentiment Identification from Online News

Published:2017-07 Issue:3 Volume:8 Page:34-44
ISSN:1947-8208
Container-title:International Journal of Knowledge and Systems Science
language:en
Short-container-title:

Author:

Chen Zhong-Yong¹,Li Wen-Ting²

Affiliation:

1. Department of Information Management, National Taiwan University, Taipei City, Taiwan

2. School of Computer Science, University of Birmingham, Birmingham, UK

Abstract

In this paper, the authors investigate the topic-independent Chinese sentiment identification problem from online news. They analyze the word usage and sentence structure of the documents for inferring representative terms and sentences in the documents, and then employ the feature values of each document for identifying the opinion of the topic-independent online news. The support vector machine (SVM) is leveraged for training the classified model in terms of the extracted features and identifying the opinion orientation of the topic-independent documents by the trained model. Experimental results demonstrated that the authors' features are helpful for identifying the opinions of the topic-independent documents, and can help readers for filtering out the negative documents.

Publisher

IGI Global

Subject

Artificial Intelligence,Management of Technology and Innovation,Information Systems and Management,Organizational Behavior and Human Resource Management,Strategy and Management,Information Systems

Reference16 articles.

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4. Introduction to Information Retrieval

5. Ku, L. W., Liang, Y. T., & Chen, H. H. (2006). Opinion Extraction, Summarization and Tracking in News and Blog Corpora. Proceedings of the AAAI Symposium on Computational Approaches to Analysing Weblogs.

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