Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

Extracting Emotional Units based on POS Templates

Volume 14, Number 2, February 2018, pp. 357-362
DOI: 10.23940/ijpe.18.02.p17.357362

Zhenggao Pan, Lili Chen

School of Information Engineering, Suzhou University, Suzhou, 234000, China



Abstract:

With the increasingly popularity of electronic commerce, a large number of product reviews appeared in electronic commerce websites, which implicated a lot of valuable business information. Sentiment analysis is the core issue in disposing of business information, and the product feature words and sentiment words extraction are key technology that affect the quality of sentiment analysis. This paper proposes a simultaneous extraction algorithm of product feature words and sentiment words based on part-of-speech(POS) relation templates. Firstly, we extract possible POS dependency templates in a training set by using the supervised sequence rules mining algorithm. Secondly, we use the templates in the test samples to extract possible two tuple of product feature words and sentiment words. Finally, we test this method in a hotel review corpus. The experimental results show that this proposed method has a good application effect.

 

References: 7

    1. S. Cen, Y. Mao, R. Li, “Credit distribution: A graph-based approach to extract product description words,” Knowledge Acquisition and Modeling, KAM’08. International Symposium on, pp.398-402, 2008.
    2. L. L. Dong, F. R. Zhao, X. Zhang, “Analysing Propensity of Product Reviews Based on Domain Ontology and Sentiment Lexicon,” Computer Applications and Software, vol. 31, no. 12, pp.104-108, 2014.
    3. J. Z. Du, J. Xu, Y. Liu, “Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis,” New Technology of Library and Information Service, vol. 30, no. 5, pp.74-82, 2014.
    4. M. Hu, B. Liu, “Opinion feature extraction using class sequential rules,” Proc. of the Spring Symposia on Computational Approaches to Analyzing Web blogs, pp. 61-66, 2006.
    5. R. Srikant, R. Agrawal, “Mining sequential patterns: generalizations and performance improvements”, Proc. of 5th International Conference on Extending Database Technology(EDBT), pp. 3-17, 1996.
    6. W. J. Zhao, Y. Zhou, “A template-based approach to extract product features and sentiment words”, IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE’09), pp. 1-5, 2009.
    7. Y. Zhang, Y. W. Liu, “Sequential Pattern Algorithm of Association Rules based on Constraint”, Journal of Taiyuan Normal University (Natural Science Edition), vol. 14, no. 1, pp. 44-48, 2015.

       

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Attachments:
      Download this file (IJPE-2018-02-17.pdf)IJPE-2018-02-17.pdf[Extracting Emotional Units based on POS Templates]523 Kb
       

      CURRENT ISSUE

      Prev Next

      Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

      Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

      Read more

      Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

      Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

      Read more

      Spark-based Ensemble Learning for Imbalanced Data Classification

      Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

      Read more

      Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

      Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

      Read more

      An Improved Algorithm based on Time Domain Network Evolution

      Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

      Read more

      Auto-Tuning for Solving Multi-Conditional MAD Model

      Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

      Read more

      Smart Mine Construction based on Knowledge Engineering and Internet of Things

      Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

      Read more

      A Mining Model of Network Log Data based on Hadoop

      Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

      Read more
      This site uses encryption for transmitting your passwords. ratmilwebsolutions.com