Graph-Based Sentiment Analysis Model for E-Commerce Websites’ Data

  • Monali BordoloiEmail author
  • Saroj Kumar Biswas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


E-Commerce has evolved tremendously in the past few years. To enhance the existing business position, the commercial sites need to understand the underlying sentiment of the customers. To do so, efficient sentiment analysis technique is highly desirable in order to deeply understand the underlying meaning and sentiment of the customers. This paper proposes an effective sentiment analysis model that makes use of graph-based keyword extraction using degree centrality measure and domain dedicated polarity assignment techniques for the advanced analysis of mobile handset reviews collected from different electronic commercial sites. The proposed model outperforms some of the existing models.


Sentiment analysis Keyword extraction Graph-based approach POS tagging 


  1. 1.
    Rana, S., Singh, A.: Comparative analysis of sentiment orientation using SVM and Naïve Bayes techniques. In: 2nd international conference on next generation computing technologies, IEEE (2016)Google Scholar
  2. 2.
    Yan, X., Huang, T.: Tibetan sentence sentiment analysis based on the maximum entropy model. In: 10th International conference on broadband and wireless computing, communication and applications, IEEE (2015)Google Scholar
  3. 3.
    Nagarajan, R., Anu, S., Nair, H.: Keyword extraction using graph based approach. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 10(10) (2016)Google Scholar
  4. 4.
    Yadav, C.S., Sharan, A., Joshi, M.L.: Semantic graph based approach for text Mining. In: International conference on issues and challenges in intelligent computing technique, IEEE (2014)Google Scholar
  5. 5.
    Sharma, A., Dey, S.: A comparative study of feature selection and machine learning techniques for sentiment analysis. RACS’12, Oct 23–26, 2012, San Antonio, TX, USA. pp. 1–7, ACM (2012) (978-1-4503-1492)Google Scholar
  6. 6.
    Tang, S., Zhang, J.: An empirical study of sentiment analysis for chinese documents. Expert Syst. Appl. 34, 2622–2629 Elsevier (2008)Google Scholar
  7. 7.
    Boiy, E., Moens, M.F.: A machine learning approach to sentiment analysis in multilingual Web texts. Inf. Retrieval 12, 526–558, Springer (2009)
  8. 8.
    Bronselaer, A., Pasi, G.: An approach to graph-based analysis of textual documents. In: 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013), Advances in Intelligent Systems Research, Atlantis Press (2013)Google Scholar
  9. 9.
    Biswas, S.K., Marbaniang, L., Purkayastha, B., Chakraborty, M., Heisnam, R.S., Bordoloi, M.: Rainfall forecasting by relevant attributes using artificial neural networks—a comparative study. Int. J. Big Data Intell. 3(2), 111–121, Inderscience (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologySilcharIndia

Personalised recommendations