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Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach

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Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12736))

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Abstract

There is a rich collection of studies exploring different aspects of consumer brand relationship. Traditional approaches of questionnaires and analysis are based on measurements collected from a relatively small number of survey participants. With the advancements in natural language processing (NLP) techniques, opportunities exist for applying NLP techniques to discover consumer brand relationship from social media platforms that possess a large amount of data on consumer opinion and sentiment. In this study, we review consumer brand relationship analysis focusing on leveraging NLP and machine learning techniques to address some challenges associated with discovering customer brand relationship from social media data and propose a methodological framework for the approach. This study has implications for both academic research and practitioners as it presents an alternative way to investigate consumer brand relationship.

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Notes

  1. 1.

    E. Ma. NLP Augmentation. https://github.com/makcedward/nlpaug, 2019.

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Author information

Authors and Affiliations

  1. University of North Florida, Jacksonville, 32224, USA

    Di Shang

  2. Shanghai University of Finance and Economics, Shanghai, 200433, China

    Zhenda Hu

  3. Singapore Management University, Singapore, 178902, Singapore

    Zhaoxia Wang

Authors
  1. Di Shang
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  2. Zhenda Hu
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  3. Zhaoxia Wang
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Corresponding author

Correspondence to Di Shang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

A Appendix

A Appendix

Consumer Brand Relationship Measurement Items adopted from [33]

  1. 1.

    I would like to recommend brand X to my friends.

  2. 2.

    Image of brand X is fit for my taste.

  3. 3.

    In product Y, no other brands can replace brand X in my heart.

  4. 4.

    Image of brand X fits my current lifestyle.

  5. 5.

    If I buy product Y next time, I would like to buy brand X again.

  6. 6.

    If brand X is out of stock, I will go to another store to look for it instead of buying other brands.

  7. 7.

    I think highly of the prospect of brand X.

  8. 8.

    Although the price of brand X is a little bit higher than other brands, I would like to choose it.

  9. 9.

    I will not regret for choosing brand X.

  10. 10.

    I would like to buy other products of brand X.

  11. 11.

    The product of brand X satisfies my request for category Y very well.

  12. 12.

    I am satisfied with this [brand].

  13. 13.

    The [brand] has come up to my expectations.

  14. 14.

    This brand is close to an ideal [brand].

  15. 15.

    I pay attention to the news about company X.

  16. 16.

    I would like to visit the website of company X.

  17. 17.

    I would like to join the brand X club to communicate with more customers of brand X.

  18. 18.

    I know the requirement of typical customers of brand X for product Y.

  19. 19.

    The communication with brand X customers makes me feel intimate.

  20. 20.

    I would like to help brand X clients rather than other brands clients.

  21. 21.

    I would like to make friends with brand X customers rather than other brands customers.

  22. 22.

    I know the differences of product attributes (such as function, appearance, capability) between brand X and other brands.

  23. 23.

    I know the product line of brand X.

  24. 24.

    I know the business scope of company X.

  25. 25.

    I know the current prices of main brand X products.

  26. 26.

    I know the development history of company X.

  27. 27.

    I think that company X is familiar with the customers requirement for product Y.

  28. 28.

    I think that company X will deal with the feedback from customers.

  29. 29.

    I believe that company X will respect the customers’ benefit.

  30. 30.

    I think that company X commitment to customers is credible.

  31. 31.

    This [brand] is reliable.

  32. 32.

    This is an honest [brand].

  33. 33.

    I trust this [brand].

  34. 34.

    I can recognize brand X only through its logo or advertising.

  35. 35.

    I can associate its advertising or logo with brand X’s name.

  36. 36.

    I feel that I understand this [brand].

  37. 37.

    The [brand] and I are meant for each other.

  38. 38.

    This [brand] reveals a lot about my personality.

  39. 39.

    This [brand] plays a decisive role in my life.

  40. 40.

    I believe that this [brand] provides sufficient options to get in touch with other consumers/users of this [brand].

  41. 41.

    It is interesting to share experiences with other consumers/users of this [brand].

  42. 42.

    I use or would like to use the option to discuss with other consumers/users of this [brand].

  43. 43.

    I am of the view that this [brand] provides sufficient options to get in touch with employees of this [brand].

  44. 44.

    It is important to me being able to contact employees of this [brand].

  45. 45.

    I use or would like to use the option to discuss about [brand] with employees of this [brand].

  46. 46.

    I think that this [brand] provides sufficient options to get in touch with the [brand] producer through interactive online applications.

  47. 47.

    It is important to me being able to get in touch with the [brand] producer through interactive online applications.

  48. 48.

    I use or would like to use the option to get in touch with the [brand] producer through interactive online applications.

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Shang, D., Hu, Z., Wang, Z. (2021). Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_47

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  • DOI: https://doi.org/10.1007/978-3-030-78609-0_47

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