期刊文献+

Words semantic orientation classification based on HowNet 被引量:4

Words semantic orientation classification based on HowNet
原文传递
导出
摘要 Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the words with intense orientations were built up. The orientation similarity between the seed words and the given word was then calculated using the sentiment weight priority to recognize the semantic orientation of common words. Finally, the words' semantic orientation and the context were combined to recognize the given words' orientation. The experiments show that the measurement approach achieves better results for common words' orientation classification and contributes particularly to the text orientation classification of large granularities. Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the words with intense orientations were built up. The orientation similarity between the seed words and the given word was then calculated using the sentiment weight priority to recognize the semantic orientation of common words. Finally, the words' semantic orientation and the context were combined to recognize the given words' orientation. The experiments show that the measurement approach achieves better results for common words' orientation classification and contributes particularly to the text orientation classification of large granularities.
出处 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2009年第1期106-110,共5页 中国邮电高校学报(英文版)
基金 supported by the National Natural Science Foundation of China (50375010).
关键词 text classification semantic orientation semantic similarity orientation weight priority HOWNET text classification, semantic orientation, semantic similarity, orientation weight priority, HowNet
  • 相关文献

参考文献2

二级参考文献13

  • 1BELKIN N J, CROFT W B. Information filtering and information retrieval: two sides of the same coin[J]. Communication of the ACM, 1992, 35(2): 29-38.
  • 2STEVENS C. Knowledge-Based Assistance for Accessing Large, Poorly Structured Information Spaces[D]. University of Colorado, Department of Computer Science, Boulder.
  • 3DOUGLAS W, OARD, et al. A conceptual framework for text filtering [EB/OL]. Technical Report CS-TR3643, http://www. clis. umd. edu/dlrg/filter/papers. ps, February 15, 2003.
  • 4LAHAM D. Latent semantic analysis approaches to categorization[A]. Proceedings of the 19th Annual Meeting of the Cognitive Science Society[C]. 1997. 979.
  • 5Vasileios Hatzivassiloglou, Kathleen R. McKeown. Predicting the semantic orientation of adjectives[A]. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Conference of the European Chapter of the ACL[C], 1997:174- 181.
  • 6Turney, Peter, Littman Michael. Measuring praise and criticism: Inference of semantic orientation from association[J]. ACM Transactions on Information Systems, 2003, 21(4): 315- 346.
  • 7Turney ,Peter. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews[A]. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics[C]. 2002:417 -424.
  • 8Bo Pang,Lillian Lee, Shivanathan Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques[A]. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing[C]. 2002:79 - 86.
  • 9Bo Pang,Lillian Lee. Seeing Stars: Exploiting Class Relationships for Sentiment Categorizalion with respect to Rating Seales[A]. ACL2005, 115-124.
  • 10K Dave, S lawrence, DM Pennock. , Mining the peanut gallery: opinion extraction and semantic classification of product reviews[A]. WWW2003, 519-28.

共引文献347

同被引文献113

  • 1刘志勇,刘磊,刘萍萍,杨帆,贾冰.一种基于语义网的个性化学习资源推荐算法[J].吉林大学学报(工学版),2009,39(S2):391-395. 被引量:14
  • 2LIN Whei Min, WU Chien Hsien, LIN Chia Hung, et al. Detection and Classification of Multiple Power-Quality Disturbances with Wavelet Muhiclass SVM [ J ]. IEEE Trans on Power Delivery, 2008, 23 (4) : 2575-2582.
  • 3TSOUMAKAS G, KATAKIS I. Multi-Label Classification: An Overview [ J ]. International Journal of Data Warehousing and Mining, 2007, 3 (3): 1-13.
  • 4ZHANG M L, ZHOU Z H. Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization[ J]. IEEE Trans on Knowledge and Data Engineering, 2006,18 ( 10 ) : 1338-1351.
  • 5ZHANG M L, ZHOU Z H. ML-kNN: A lazy learning approach to multi-label learning [ J ]. Pattern Recognition, 2007,40(7) : 2038-2048.
  • 6ELISSEEFF A, WESTON J. A kernel method for multilabelled classification [ EB/OL ]. [ 2010-03-12 ]. http :// books. nips. cc/papers/files/nips14/AA45. pdf.
  • 7Transmission and Distribution Committee of the IEEE Power & Energy Society. IEEE Std. 1159-2009 IEEE Recommended Practice for Monitoring Electric Power Quality [ S ]. New York : the Institute of Electrical and Electronics Engineers, Inc. ,2009.
  • 8PANIGRAHI B K, PANDI V. R. Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm[ J]. IET Generation, Transmission & Distribution, 2009, 3 (3) : 296-306.
  • 9UYAR M, YILDIRIM S, GENCOGLU M T,. An effective wavelet-based feature extraction method for classification of power quality disturbance signals [ J ]. Electric Power Systems Research,2008,78(10) : 1747-1755.
  • 10Bruce R F,Wiebe J . Recognizing subjectivity: A case study in manual tagging[ J]. Natural Language Engineering,1999,5(02) 187-205.

引证文献4

二级引证文献58

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部