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A novel feature extraction methodology for sentiment analysis of product reviews

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Abstract

Feature extraction is one of the key steps for text sentiment analysis (SA), and the corresponding algorithms have important effect on the results. In the paper, a novel methodology is proposed to extract the feature for SA of product reviews. First, based on the diversified expression forms of product reviews, the generalized TF–IDF feature vectors are obtained by introducing the semantic similarity of synonyms. Then, in view of the different lengths of product reviews, the local patterns of the feature vectors are identified with OPSM biclustering algorithm. Finally, we improve PrefixSpan algorithm to detect the frequent and pseudo-consecutive phrases with high discriminative ability (namely FPCD phrases), which contain word-order information. Furthermore, some important factors, such as the separation and discriminative ability of words, are also employed to improve the discriminative ability of sentiment polarity. Based on the previous steps, the text feature vectors are extracted. A series of the experiment and comparison results indicate that the performance for SA on product review is greatly improved.

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Acknowledgements

The authors thank gratefully for the colleagues participated in this work and provided technical supports. This work is supported by Guangdong Provincial Engineering Technology Research Center for Data Science (Nos. 2016KF09, 2016KF10), and the National Statistical Science Research Project of China (Nos. 2015LY81, 2016LY98). This work was also supported by the Science and Technology Department of Guangdong Province in China (Grant Nos. 2016A010101020, 2016A010101021, 2016A010101022), the grant from Guangdong Province Science and Technology Planning Project (No. 2013B040404009), Foundation of Guangdong Polytechnic of Science and Technology (No. XJSC2016206), Natural Science Funds of Shenzhen Science and Technology Innovation Commission (No. JCYJ20160527172144272) and the Innovation Project of Graduate School of South China Normal University (No. 2015lkxm37).

Author information

Authors and Affiliations

  1. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China

    Xin Chen, Yun Xue, Xin Lu, Xiaohui Hu & Zhihao Ma

  2. Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, Guangdong Provincial Engineering Technology Research Center for Data Science, Guangzhou, 510006, China

    Yun Xue

  3. Shenzhen PolyTechnic, Shenzhen, 518055, China

    Xin Chen & Hongya Zhao

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  1. Xin Chen

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  2. Yun Xue

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  3. Hongya Zhao

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  4. Xin Lu

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  5. Xiaohui Hu

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  6. Zhihao Ma

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Correspondence toYun Xue.

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Chen, X., Xue, Y., Zhao, H.et al. A novel feature extraction methodology for sentiment analysis of product reviews.Neural Comput & Applic31, 6625–6642 (2019). https://doi.org/10.1007/s00521-018-3477-2

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