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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.

       

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