Consumer Satisfaction Rating System Using Sentiment Analysis

  • Kumar GauravEmail author
  • Prabhat Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10595)


Owing to the inclination towards e-Commerce, the importance of consumer reviews has evolved significantly. Potential consumers exhibit sincere intents in seeking opinions of other consumers who have already had a usage experience of the products they are intending to make a purchase decision on. The underlying businesses also deem it fit to ascertain common public opinions regarding the quality of their products as well as services. However, the consumer reviews have bulked over time to such an extent that it has become a highly challenging task to read all the reviews, even if limiting to only the top ones, to reach an informed purchase decision or have an insight regarding how satisfied or not the consumers of a particular product are. Since most of the reviews are either unstructuctured or semi-structured, information classification is employed to derive knowledge from the reviews. However, most classification methods based on sentiment orientation of reviews do not detect which features of a product were specifically liked or disliked by a reviewer. This constitutes itself into another problem since most consumers are on the lookout for certain prerequisite features while viewing the products. The paper proposes a Consumer Satisfaction Rating System (CSRS) based on sentiment analysis of consumer reviews in context of the features of a product. The system aims at providing a summary that represents the extent to which the consumers were satisfied or unsatisfied with the specific features of a product.


e-Commerce Online reviews Product features Sentiment analysis Opinion mining 


  1. 1.
    Bollen, J., Mao, H.: Twitter mood predicts the stock market 2, pp. 1–8 (2011)Google Scholar
  2. 2.
  3. 3.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. found. trends®. Inf. Retr. 1, 91–231 (2006)Google Scholar
  4. 4.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)CrossRefGoogle Scholar
  5. 5.
    Srivastava, A., Singh, M.P., Kumar, P.: Supervised semantic analysis of product reviews using weighted k-NN classifier. In: 11th International Conference on Information Technology: New Generations (ITNG), pp. 502–507. IEEE, Las Vegas (2014)Google Scholar
  6. 6.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 04, pp. 168–177 (2004)Google Scholar
  7. 7.
    Popescu, A.-M., Etzioni, O.: Extracting product features and opinion from reviews. Hum. Lang. Technol. Empir. Methods Nat. Lang. Process. Vancouver, Br. Columbia, pp. 339–346 (2005)Google Scholar
  8. 8.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: ACL-02 Conference on Empirical Methods in Natural Language Processing - EMNLP 2002 (2002)Google Scholar
  9. 9.
    Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, M.Á., Moreno, V., Fraga, A., Sánchez-Cervantes, J.L.: Feature-based opinion mining through ontologies. Expert Syst. Appl. 41, 5995–6008 (2014)CrossRefGoogle Scholar
  10. 10.
    Fan, Z.-P., Che, Y.-J., Chen, Z.-Y.: Product sales forecasting using online reviews and historical sales data: a method combining the Bass model and sentiment analysis. J. Bus. Res. 74, 90–100 (2017)CrossRefGoogle Scholar
  11. 11.
    Tiwari, P., Mishra, B.K., Kumar, S., Kumar, V.: Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. Int. J. Know. Discov. Bioinf. 7, 30–41 (2017)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Bi, J.-W., Fan, Z.-P.: Ranking products through online reviews: a method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf. Fusion 36, 149–161 (2017)CrossRefGoogle Scholar
  13. 13.
    Villaroel Ordenes, F., Ludwig, S., Grewal, D., de Ruyter, K., Wetzels, M.: Analyzing online reviews through the lens of speech act theory: implications for consumer sentiment analysis. J. Consum. Res. 189, 227–246 (2016)Google Scholar
  14. 14.
    Qiu, G., Liu, B., Chen, C.: Opinion word expansion and target extraction through double propagation. Assoc. Comput. Linguist. 37, 9–27 (2011)CrossRefGoogle Scholar
  15. 15.
    Santorini, B.: Part-of-speech tagging guidelines for the Penn Treebank Project (3rd revision) (1990)Google Scholar
  16. 16.
    Kumar, S., Kumar, P., Singh, M.P.: A generalized procedure of opinion mining and sentiment analysis. In: International Conference on Recent Trends in Communication and Computer Networks (ComNet), pp. 105–108, Hyderabad (2013)Google Scholar
  17. 17.
    Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc. (2009)Google Scholar
  18. 18.
    Walt, S., Colbert, S.C., Varoquaux, G.: The Numpy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22–30 (2011)CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: 7th Conference on International Language Resources and Evaluation (LREC 2010), pp. 2200–2204 (2010)Google Scholar
  21. 21.
    Kumar, P., Dasari, Y., Nath, S., Sinha, A.: Controlling and mitigating targeted socio-economic attacks. In: Dwivedi, Y.K., et al. (eds.) I3E 2016. LNCS, vol. 9844, pp. 471–476. Springer, Cham (2016). doi: 10.1007/978-3-319-45234-0_42 CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology PatnaPatnaIndia

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