A Step Further in Sentiment Analysis Application in Marketing Decision-Making

  • Erick Kauffmann
  • David Gil
  • Jesús PeralEmail author
  • Antonio Ferrández
  • Ricardo Sellers
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Nowadays, firms have realized the importance of Big Data, highlighting the need for understanding the current state of marketing practice with respect to Big Data analytics. Among the different sources of Big Data, User-Generated Content (UGC) is one of the most important ones. From blogs to social media and online reviews, consumers generate huge amounts of brand related information that have a decisive potential business value in targeted advertising, customer engagement or brand communication, among others. In the same line, previous empirical findings show that UGC has significant effects on brand images, purchase intentions, and sales. It plays an important role for customers’ potential buying decisions. Thus, mining and analysing UGC data such as comments and sentiments might be useful for firms. Particularly, brand management can be one area of interest, as online reviews might have an influence on brand image and brand positioning. Within this context, as well as the quantitative star score usual in this UGC, in which the buyers rate the product, a recent stream of research employs Sentiment Analysis (SA) tools with the aim of examining the textual content of the review and categorizing buyers’ opinions. While certain SA split the comments into two classes (negative or positive), other incorporate more sentiment classes. However, the review can have phrases with different polarities because the user can have different experiences and sentiments about each feature of the product. Finding the polarity of each feature can be interesting for the decision makers of a product. In this paper, we consider that although these two scores (star and sentiment) are related, the sentiment score highlights extra information not detailed in the star score, which is crucial to be extracted in order to have better criteria of comparison between products. Moreover, we mine the positive and negative features of the products analysing the sentiment.



This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32, the Project RESCATA under Grant TIN2015-65100-R, the Project PROMETEO/2018/089, and the Lucentia AGI Grant.


  1. 1.
    Abbasi, A., France, S., Zhang, Z., Chen, H.: Selecting attributes for sentiment classification using feature relation networks. IEEE Trans. Knowl. Data Eng. 23(3), 447 462 (2011)CrossRefGoogle Scholar
  2. 2.
    Archak, N., Ghose, A., Ipeirotis, P.: Show me the Money! Deriving the pricing power of product features by mining consumer reviews. ACM (2007). 978-1-59593-609-7/07/0008Google Scholar
  3. 3.
    Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016 Mar–Apr)CrossRefGoogle Scholar
  4. 4.
    Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. Intell. Syst. IEEE. 28, 15–21 (2013). Scholar
  5. 5.
    Chevalier, J., Mayzlin, D.: The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43(August), 345–354 (2006)CrossRefGoogle Scholar
  6. 6.
    Dellarocas, C.: The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manage. Sci. 49, 1407–1424 (2003)CrossRefGoogle Scholar
  7. 7.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  8. 8.
    García-Moya, L., Anaya-Sánchez, H., Berlanga-Llavori, R.: Retrieving product features and opinions from customer reviews. Intell. Syst. IEEE 28, 19–27 (2013). Scholar
  9. 9.
    Godes, David, Mayzlin, Dina: Using On-line conversations to study word-of-mouth communication. Mark. Sci. 23(4), 545–560 (2004)CrossRefGoogle Scholar
  10. 10.
    Haddi, E., Liu, X., & Shi, Y.: The role of text pre-processing in sentiment analysis. ITQM (2013)Google Scholar
  11. 11.
    He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering (2016).
  12. 12.
    Joshi, M., Prajapati, P., Shaikh, A., Vala, V.: A survey on Sentiment Analysis. Int. J. Comput. Appl. 163(6), 34–39 (2017). Scholar
  13. 13.
    Liske, D.: Tidy sentiment analysis in R (2018).
  14. 14.
    Noone, B.M., McGuire, K.A.: Effects of price and user-generated content on consumers’ prepurchase evaluations of variably priced services. J. Hosp. Tour. Res. 38(4), 562–581 (2014)CrossRefGoogle Scholar
  15. 15.
    Paknejad, S.: Sentiment classification on Amazon reviews using machine learning approaches. Dissertation (2018)Google Scholar
  16. 16.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  17. 17.
    Paracchini, P.: “Sentiment analysis using the tidytext package (2016).
  18. 18.
    Ravendra, R.S.J.: A proposed novel approach for sentiment analysis and opinion mining. Int. J. UbiComp. 5, 1–10 (2014)Google Scholar
  19. 19.
    Singh, J., Irani, S., Rana, N., Dwivedi, Y., Saumya, S., Roy, P.: Predicting the helpfulness of online consumer reviews. J. Bus. Res. 70, 345–355 (2017). Scholar
  20. 20.
    Singla, Z., Randhawa, S., Jain, S.: Sentiment analysis of customer product reviews using machine learning. In: 2017 International Conference on Intelligent Computing and Control (I2C2), 1–5 (2017)Google Scholar
  21. 21.
    Sun, T., Youn, S., Wu, G., Kuntaraporn, M.: Online word-of-mouth: an exploration of its antecedents and consequences. J. Comput. Mediat. Commun. 11(4), 1104–1127 (2006)CrossRefGoogle Scholar
  22. 22.
    Tsang, A.S.L., Prendergast, G.: Is a star worth a thousand words? The interplay between product-review texts and rating valences. Eur. J. Mark. 43(11/12), 1269–1280 (2009)CrossRefGoogle Scholar
  23. 23.
    Wang, J., Wang, L., Wang, M.: Understanding the effects of eWOM social ties on purchase intentions: a moderated mediation investigation. Electron. Commer. Res. Appl. 28, 54–62 (2018). Scholar
  24. 24.
    Xu, X., Wang, X., Li, Y., Haghighi, M.: Business intelligence in online customer textual reviews: understanding consumer perceptions and influential factors. Int. J. Inf. Manage. 37(6), 673–683 (2017)CrossRefGoogle Scholar
  25. 25.
    Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of the 3rd IEEE International Conference on Data Mining ICDM-2003, pp. 427–434 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erick Kauffmann
    • 1
    • 2
  • David Gil
    • 1
  • Jesús Peral
    • 1
    Email author
  • Antonio Ferrández
    • 1
  • Ricardo Sellers
    • 1
  1. 1.University of AlicanteSan Vicente del Raspeig, AlicanteSpain
  2. 2.University of Costa RicaSan JoséCosta Rica

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