A Multi-Semantic Classification Model of Reviews Based on Directed Weighted Graph

  • Shaozhong Zhang
  • William Wei Song
  • Minjie Ding
  • Ping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10042)


Semantic and sentimental analysis plays an important role in natural language processing, especially in textual analysis, and has a wide range of applications in web information processing and management. This paper intends to present a sentimental analysis framework based on the directed weighted graph method, which is used for semantic classification of the textual comments, i.e. user reviews, collected from the e-commerce websites. The directed weighted graph defines a formal semantics lexical as a semantic body, denoted to be a node in the graph. The directed links in the graph, representing the relationships between the nodes, are used to connect nodes to each other with their weights. Then a directed weighted graph is constructed with semantic nodes and their interrelationships relations. The experimental results show that the method proposed in the paper can classify the semantics into different classification based on the computation of the path lengths with a threshold.


Directed weighted graph Reviews Semantic classification 


  1. 1.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Berlin (2008)MATHGoogle Scholar
  2. 2.
    Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, 2nd edn. Chapman & Hall, London (2010)Google Scholar
  3. 3.
    Cambria, E., Grassi, M., Hussain, A., Havasi, C.: Sentic computing for social media marketing. Multimedia Tools Appl. 59(2), 557–577 (2012)CrossRefGoogle Scholar
  4. 4.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012). doi:10.2200/S00416ED1V01Y201204HLT016 CrossRefGoogle Scholar
  5. 5.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: HLT 2005 Proceedings of Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, pp. 347–354, October 2005Google Scholar
  6. 6.
    Hobbs, J.R., Riloff, E.: Information extraction. In: Handbook of Natural Language Processing, pp. 1–31 (2010)Google Scholar
  7. 7.
    Bunescu, R.C., Mooney, R.J.: Subsequence kernels for relation extraction. In: Advances in Neural Information Processing Systems, pp. 171–178 (2005)Google Scholar
  8. 8.
    Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2008)CrossRefGoogle Scholar
  9. 9.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project report, Stanford (2009)Google Scholar
  10. 10.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM Press, New York (2004)Google Scholar
  12. 12.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  13. 13.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL-2002, 40th Annual Meeting of Association for Computational Linguistics, USA, pp. 417–424 (2002)Google Scholar
  14. 14.
    Venugopalan, M., Gupta, D.: Exploring sentiment analysis on twitter data. In: 2015 Eighth International Conference on Contemporary Computing (IC3), Noida, pp. 241–247, 20–22 August 2015Google Scholar
  15. 15.
    Li, G., Liu, F.: A clustering-based approach on sentiment analysis. In: 2010 International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Hangzhou, pp. 331–337, 15–16 November 2010Google Scholar
  16. 16.
    Liu, R., Xiong, R., Song, L.: A sentiment classification method for Chinese document. In: 2010 5th International Conference on Computer Science and Education (ICCSE), Hefei, 24–27 August 2010, pp. 918–922 (2010)Google Scholar
  17. 17.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of 7th Conference on International Language Resources and Evaluation (LREC 2010), pp. 1320–1326, May 2010Google Scholar
  18. 18.
    Kao, H.Y., Lin, Z.Y.: A categorized sentiment analysis of chinese reviews by mining dependency in product features and opinions from blogs. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Toronto, ON, vol. 1, 456–459, 31 August – 3 September 2010Google Scholar
  19. 19.
    McAuley, J., Targett, C., Shi, J., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR 2015 Proceedings of 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM, New York (2015)Google Scholar
  20. 20.
    McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: KDD 2015 Proceedings of 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM. New York (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shaozhong Zhang
    • 1
  • William Wei Song
    • 2
  • Minjie Ding
    • 1
  • Ping Hu
    • 1
  1. 1.School of Electronic and Computer ScienceZhejiang Wanli UniversityNingboChina
  2. 2.Information Systems and Business IntelligenceDalarna UniversityBorlängeSweden

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