Recognition of Comparative Sentences from Online Reviews Based on Multi-feature Item Combinations

  • Jie Zhang
  • Liping Zheng
  • Lijuan ZhengEmail author
  • Junyan Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


At present, comparative sentences in online reviews are a common and convincing expression. In the autonomous recognition of Chinese comparative sentences, the selection of feature items plays a important role. The previous research mainly adopt the pattern recognition methods. This paper focuses on the recognition of comparative sentences for multi-feature item combinations in online reviews and use the text classification algorithm in machine learning to achieve. First, analyze the influence of the number of different feature items in comparative sentence recognition about the classification performance, and select the number of feature items with the highest mean of classification accuracy, make a combination of different feature items. Then use the document frequency method to reduce the dimension of feature items and select the Boolean weights to construct feature vector. Finally, using SVM classifier to discern comparative sentences. Based on the online reviews of mobile phone, This paper studies the recognition of comparative sentences for thirty feature items.


Feature items Compare elements Word frequency Accuracy 



This work is supported by the National Institute of Education Humanities and Social Sciences Research Youth Fund Project (16YJCZH159), Shandong Provincial Institute of Humanities and Social Sciences Research Project (J16YF25), Liaocheng University Scientific Research Project (31801140).


  1. 1.
    Xiong, D.L., Cheng, J.M., Tian, S.L.: Sentence orientation research based on How Net. Comput. Eng. Appl. 44(22), 143–145 (2008)Google Scholar
  2. 2.
    Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 244–251. ACM (2006)Google Scholar
  3. 3.
    Jindal, N., Liu, B.: Mining comparative sentences and relations. In: Proceeding of AAAI, Palo Alto, pp. 1331–1336 (2006)Google Scholar
  4. 4.
    Doran, C., Egedi, D., Hockey, B.A., et al.: XTAG system: a wide coverage grammar for English. In: Proceedings of the 15th Conference on Computational Linguistics, vol. 2, pp. 922–928. Association for Computational Linguistics (1994)Google Scholar
  5. 5.
    Zhou, H., Hou, M., Hou, M., et al.: Chinese comparative sentences identification and comparative elements extraction based on semantic classification. J. Chin. Inf. Process. 28(3), 136–141 (2014)Google Scholar
  6. 6.
    Bai, L., Hu, R., Liu, Z.: Recognition of comparative sentences based on syntactic and semantic rules-system. Acta Scientiarum Naturalium Universitatis Pekinensis 51(2), 275–281 (2015)Google Scholar
  7. 7.
    Wu, C., Wei, X.F.: Opinion analysis and recognition of comparative sentences in user views. Comput. Sci. 43(s1), 435–439 (2016)Google Scholar
  8. 8.
    Wang, W., Zhao, T., Bing, X.U., et al.: Automatic identify Chinese comparative sentences. Intell. Comput. Appl. 5, 1–3 (2015)Google Scholar
  9. 9.
    Zheng, L.J., Wang, H.W.: Sentimental polarity and strength of online cellphone reviews based on sentiment ontology. J. Ind. Eng. Eng. Manag. 31(2), 47–54 (2017)MathSciNetGoogle Scholar
  10. 10.
    Zheng, L.J., Wang, H.W., Gao, S.: Sentimental feature selection for sentiment analysis of Chinese online reviews. Int. J. Mach. Learn. Cybernet. 9(1), 75–84 (2018)CrossRefGoogle Scholar
  11. 11.
    Wang, H.W., Zheng, L.J., et al.: Sentiment feature selection from Chinese online reviews based on statistical machine learning. In: Academic Annual Conference of China Branch of Information Systems Association (2011)Google Scholar
  12. 12.
    Zheng, L.J., Wang, H.W., et al.: Sentiment intensity of online reviews based on fuzzy-statistics sentiment words. J. Syst. Manage. 32(4), 376–384 (2013)Google Scholar
  13. 13.
    Hwee, T.N., Wei, B.G., Kok, L.L.: Feature selection, perceptron learning and a usability case study for text categorization. In: Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 31, pp. 67–73 (1997)Google Scholar
  14. 14.
    Liu, X.: A study on affective polarity classification based on statistical natural language. Tongji university master’s thesis (2011)Google Scholar
  15. 15.
    Pang, B., Lee, L., Vaithyanathan, S.: Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, US, pp. 79–86 (2002)Google Scholar
  16. 16.
    Xia, H.S., Peng, L.Y.: SVM-based comments classification and mining of virtual community: for case of sentiment classification of hotel reviews. In: Proceedings of the International Symposium on Intelligent Information Systems and Applications (IISA 2009), vol. 10, pp. 507–511 (2009)Google Scholar
  17. 17.
    Li, J.: An approach of sentiment classification using SVM for Chinese texts. In: Proceedings of 2006 International Conference on Artificial Intelligence - 50 Years’ Achievements, Future Directions and Social Impacts, pp. 759–761 (2006)Google Scholar
  18. 18.
    Shi, W., Qi, G.Q., Meng, F.J.: Sentiment classification for book reviews based on SVM model. In: Proceedings of the 2005 International Conference on Management Science and Engineering, pp. 214–217 (2005)Google Scholar
  19. 19.
    Li, X.Y., Zhang, X.F., Shen, L.: A selection means on the paramrter of radius basis function. Acta Electronica Sinica 33(B12), 2459–2463 (2005)Google Scholar
  20. 20.
    Bian, Z.Q.: Pattern Recognition. Tsinghua University Press, Beijing (1998)Google Scholar
  21. 21.
    Gao, S., Wang, H.W., Feng, G., et al.: Review of comparative opinions mining studies of online comments. New Technol. Libr. Inf. Serv. 32(10), 1–12 (2016)Google Scholar
  22. 22.
    Wang, S., Zhao, C., Liu, H.: Comparison element ellipsis identification based on rules and sequence patterns. J. Shanxi Univ. 38(1), 85–92 (2015)Google Scholar
  23. 23.
    Lu, Y.H.: A sentence similarity calculation method based on Word2Vector and edit distance. Comput. Knowl. Technol. 13(5), 146–147 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jie Zhang
    • 1
  • Liping Zheng
    • 1
  • Lijuan Zheng
    • 2
    Email author
  • Junyan Ge
    • 3
  1. 1.School of Computer ScienceLiaocheng UniversityLiaochengChina
  2. 2.School of BusinessLiaocheng UniversityLiaochengChina
  3. 3.School of Data Science and Software EngineerQingdao UniversityQingdaoChina

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