Skip to main content
SpringerLink
Log in

Sentiment Analysis to Support Marketing Decision Making Process: A Hybrid Model

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 (FTC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1289))

Included in the following conference series:

Abstract

Marketers aim to understand what influences people’s decisions when purchasing products and services, which has been proven to be based on natural instincts that drive humans to follow the behavior of others. Thus, this research is investigating the use of sentiment analysis techniques and proposes a hybrid approach that combines lexicon-based and machine learning-based approaches to analyze customers’ review a major e-commerce platform. The lexicon approach was firstly applied at a word-level to explore the reviews and provide some preliminary results about the most frequent words used in the reviews in a form of word-clouds. Then, the lexicon approach was applied to sentence-level to obtain sentiment polarity results, which was used to train machine learning models. Next, the trained models were tested on un-labelled reviews (test data); proving that Naïve Bayes (NB) outperformed other classifiers. The hybrid model described in this research can offer organizations a better understanding of customers’ attitudes towards their products.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Willcox, M.: The Business of Choice: Marketing to Consumers’ Instincts. Pearson FT Press, Upper Saddle River (2015)

    Google Scholar 

  2. Howells, K., Ertugan, A.: Applying fuzzy logic for sentiment analysis of social media network data in marketing. Proc. Comput. Sci. 120, 665 (2017)

    Article  Google Scholar 

  3. Wu, S., Chiang, R., Chang, H.: Applying sentiment analysis in social web for smart decision support marketing. J. Ambient Intell. Humanized Comput. 1–10 (2018)

    Google Scholar 

  4. Verhoef, P.C., Kooge, E., Walk, N.: Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge, London (2016)

    Google Scholar 

  5. Mäntylä, M., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16–32 (2018)

    Article  Google Scholar 

  6. Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28, 15–21 (2013)

    Article  Google Scholar 

  7. Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd international conference on World Wide Web - WWW 2013, pp. 607–618 (2013)

    Google Scholar 

  8. Gupta, E., Kumar, A., Kumar, M.: Sentiment analysis: a challenge. Int. J. Eng. Technol. 7(2.27), 291 (2018)

    Google Scholar 

  9. Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl. Based Syst. 89, 14–46 (2015)

    Article  Google Scholar 

  10. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  11. Behdenna, S., Barigou, F., Belalem, G.: Document level sentiment analysis: a survey. EAI Endorsed Trans. Context Aware Syst. Appl. 4 (2018)

    Google Scholar 

  12. Yessenalina, A., Yue, Y., Cardie, C.: Multi-level structured models for document-level sentiment classification. In: EMNLP (2010)

    Google Scholar 

  13. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 84 (2013)

    Article  Google Scholar 

  14. Wang, H., Yin, P., Zheng, L., Liu, J.: Sentiment classification of online reviews: using sentence-based language model. J. Exp. Theor. Artif. Intell. 26(1), 13–31 (2013)

    Article  Google Scholar 

  15. Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 814 (2016)

    Article  Google Scholar 

  16. Vanaja, S., Belwal, M.: Aspect-Level sentiment analysis on e-commerce data. In: International Conference on Inventive Research in Computing Applications (ICIRCA), p. 1276 (2018)

    Google Scholar 

  17. Poria, S., Cambria, E., Winterstein, G., Huang, G.: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl. Based Syst. 69, 46 (2014)

    Google Scholar 

  18. Deng, S., Sinha, A., Zhao, H.: Adapting sentiment lexicons to domain-specific social media texts. Decis. Support Syst. 94, 66 (2017)

    Article  Google Scholar 

  19. Khoo, C., Johnkhan, S.: Lexicon-based sentiment analysis: comparative evaluation of six sentiment lexicons. J. Inf. Sci. 44(4), 491–511 (2017)

    Article  Google Scholar 

  20. Vu, L., Le, T.: A lexicon-based method for sentiment analysis using social network data. In: International Conference Information and Knowledge Engineering (IKE 2017) (2017)

    Google Scholar 

  21. Cho, H., Kim, S., Lee, J., Lee, J.: Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews. Knowl. Based Syst. 71, 61–71 (2014)

    Article  Google Scholar 

  22. Asghar, M., Khan, A., Ahmad, S., Qasim, M., Khan, I.: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS ONE 12(2), e0171649 (2017)

    Article  Google Scholar 

  23. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  24. Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 27–38 (2019)

    Google Scholar 

  25. Goel, A., Gautam, J., Kumar, S.: Real time sentiment analysis of tweets using Naive Bayes. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (2016)

    Google Scholar 

  26. Dey, L., Chakraborty, S., Biswas, A., Bose, B., Tiwari, S.: Sentiment analysis of review datasets using Naïve Bayes’ and K-NN classifier. Int. J. Inf. Eng. Electron. Bus. 8(4), 54–62 (2016)

    Google Scholar 

  27. Pratama, Y., Roberto Tampubolon, A., Diantri Sianturi, L., Diana Manalu, R., Frietz Pangaribuan, D.: Implementation of sentiment analysis on Twitter using Naïve Bayes algorithm to know the people responses to debate of DKI Jakarta governor election. J. Phys: Conf. Ser. 1175, 012102 (2019)

    Google Scholar 

  28. Singh, V., Piryani, R., Uddin, A., Waila, P., Marisha.: Sentiment analysis of textual reviews; evaluating machine learning, unsupervised and SentiWordNet approaches In: 2013 5th international conference on knowledge and smart technology (KST), pp. 122–127. IEEE (2013)

    Google Scholar 

  29. Pannala, N., Nawarathna, C., Jayakody, J., Rupasinghe, L., Krishnadeva, K.: Supervised learning based approach to aspect based sentiment analysis. In: IEEE International Conference on Computer and Information Technology (CIT) (2016)

    Google Scholar 

  30. Xia, R., Xu, F., Zong, C., Li, Q., Qi, Y., Li, T.: Dual sentiment analysis: considering two sides of one review. IEEE Trans. Knowl. Data Eng. 27(8), 2120–2133 (2015)

    Article  Google Scholar 

  31. Cruz, N., Taboada, M., Mitkov, R.: A machine-learning approach to negation and speculation detection for sentiment analysis. J. Assoc. Inf. Sci. Technol. 67(9), 2118–2136 (2015)

    Article  Google Scholar 

  32. Xia, R., Xu, F., Yu, J., Qi, Y., Cambria, E.: Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf. Process. Manage. 52(1), 36–45 (2016)

    Article  Google Scholar 

  33. Dhaoui, C., Webster, C., Tan, L.: Social media sentiment analysis: lexicon versus machine learning. J. Consum. Market. 34(6), 480–488 (2017)

    Google Scholar 

  34. Sankar, H., Subramaniyaswamy, V.: Investigating sentiment analysis using machine learning approach. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS) (2017)

    Google Scholar 

  35. Thakkar, H., Patel, D.: Approaches for sentiment analysis on Twitter: a state-of-art study. In: Proceedings of the International Network for Social Network Analysis Conference, Xi’an, China (2013)

    Google Scholar 

  36. Zhang, L., et al.: Combining lexicon-based and learning-based methods for Twitter sentiment analysis. Technical Report HPL-2011–89 (2011)

    Google Scholar 

  37. Mukwazvure, A., Supreethi, K.: A hybrid approach to sentiment analysis of news comments. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (2015)

    Google Scholar 

  38. Alhumoud, S., Albuhairi, T., Alohaideb, W.: Hybrid sentiment analyser for Arabic Tweets using R. In: 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), pp. 417–424 (2015)

    Google Scholar 

  39. Kaggle.com. Consumer reviews of Amazon products (2019). https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products

  40. Masters, K.: 89% of consumers are more likely to buy products from Amazon than other e-commerce sites: study. Forbes.com. (2019). https://www.forbes.com/sites/kirimasters/2019/03/20/study-89-of-consumers-are-more-likely-to-buy-products-from-amazon-than-other-e-commerce-sites/#dae18c64af1e. Accessed 21 Dec 2019

  41. Marbán, O., Segovia, J., Menasalvas, E., Fernández-Baizán, C.: Toward data mining engineering: a software engineering approach. Inf. Syst. 34(1), 87–107 (2009)

    Article  Google Scholar 

  42. Marbán, O., Mariscal, G., Segovia, J.: A data mining & knowledge discovery process model. In: Data Mining and Knowledge Discovery in Real Life Applications, Vienna: I-Tech (2009)

    Google Scholar 

  43. Ramasubramanian, K., Singh, A.: Machine Learning Using R. Apress, Chapter 4 (2016)

    Google Scholar 

  44. Naldi, M.: A review of sentiment computation methods with R packages. https://arxiv.org/pdf/1901.08319v1.pdf (2019)

  45. Sammut, C., Webb, G.: Encyclopedia of Machine Learning. Springer, New York (2011)

    MATH  Google Scholar 

  46. Nadali, A., Kakhky, E., Nosratabadi, H.: Evaluating the success level of data mining projects based on CRISP-DM methodology by a Fuzzy expert system. In: 2011 3rd International Conference on Electronics Computer Technology (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Brunel University London, London, UB8 3PH, UK

    Alaa Marshan & Georgia Kansouzidou

  2. University of Surrey, Guildford, GU2 7XH, UK

    Athina Ioannou

Authors
  1. Alaa Marshan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Georgia Kansouzidou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Athina Ioannou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alaa Marshan .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Marshan, A., Kansouzidou, G., Ioannou, A. (2021). Sentiment Analysis to Support Marketing Decision Making Process: A Hybrid Model. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 . FTC 2020. Advances in Intelligent Systems and Computing, vol 1289. Springer, Cham. https://doi.org/10.1007/978-3-030-63089-8_40

Download citation

Publish with us

Policies and ethics

Search

Navigation