Vol. 1 No. 2 (2022): The QUEST: Journal of Multidisciplinary Research and Development
Articles

Utilizing Google Map Reviews and Sentiment Analysis: Knowing Customer Experience in Coffee Shops

  • Mary Anne Sahagun,
  • Jun Flores,
  • Joefil Jocson
Mary Anne Sahagun
Don Honorio Ventura State University
Jun Flores
Don Honorio Ventura State University, Bacolor, Pampanga, 2001, Philippine
Joefil Jocson
Nueva Ecija University of Science and Technology, Nueva Ecija, Philippines. 3104
The Quest Vol1No2-2022
DOI: https://doi.org/10.60008/thequest.v1i2.29

Published 12/30/2022

Keywords

  • Bag-of-word,
  • ewom,
  • Customer review,
  • opinion mining,
  • sentiment analysis,
  • Vader algorithm
    • ...More
    Less

How to Cite

Sahagun, M. A., Flores, J., & Jocson, J. (2022). Utilizing Google Map Reviews and Sentiment Analysis: Knowing Customer Experience in Coffee Shops. The QUEST: Journal of Multidisciplinary Research and Development, 1(2). https://doi.org/10.60008/thequest.v1i2.29

Copyright (c) 2023 The QUEST: Journal of Multidisciplinary Research and Development

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Electronic word of mouth (eWOM) is a good source of information, and this includes customer reviews. Through this review, consumers make informed decisions. In this study, the researchers utilized Google Maps Reviews of customers of three known coffee shops. A google map review scraper was used to extract all customer's reviews and star ratings. In order to extract important information from reviews, opinion mining was done. MATLAB R2022a was used for sentiment analysis and opinion pre-processing. Each coffee shop's most popular words are represented using the Bigram model and the bag-of-words technique. This allows for the visual identification of the unique characteristics of these coffee businesses. According to the study's findings, coffee shop B had the most positive average percentage sentiment score (73%), while coffee shop C had the least negative average sentiment score. The Bigram model shows that customers enjoy the coffee these three coffee shops serve. However, when it comes to taste, location, bread, and pastries, coffee shop C has the most words. Lastly, the correlation values for star ratings vs sentiment scores for coffee shops A and B are r=0.4726 and r=0.4812. There is absolutely no association between sentiment score and star ratings for coffee shop C.

Full Paper

References

  1. B. Liu. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5, no, 1–167.
  2. Boutet, A., Kim, H. H., & Yoneki, and E. (2012). What’s in twitter: i know what parties are popular and who you are supporting now! Proceedings of the 2012 IEEE/ACM In- Ternational Conference on Advances in Social Networks Analysis and Mining, 132–139.
  3. Cheung, C. M. K., Lee, M. K. O., & D.R. Thadani. (2009). The impact of positive electronic word- of-mouth on consumer online purchasing decision. World Summit on Knowledge Society, 501–510.
  4. Dixit, S., Badgaiyan, A. J., & Khare, A. (n.d.). An integrated model for predicting
  5. consumer’s intention to write online reviews. J. Retailing Consum.Serv46, 112–120.
  6. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89.
  7. Greco, F., & & Polli, A. (2019). Emotional text mining: Customer profiling in brand man- agement. International Journal of Information Management.
  8. Ohk, K., & M. Kim. (2018). Who’s leading China’s E-commerce industry? The antecedents and consequences of E-WOM focusing on one person media. J. Theor. Appl. Inf. Technol., 96 (5), 1323–1333.
  9. Pereira, H. G., Cardoso, M., & P. Dionísio. (2017). The determinants of website purchases: the role of e-customer loyalty and word-of-mouth. Int. J. Electron. Market. Retailing, 8 (2) (201.
  10. Rathore, A. K., & & Ilavarasan, P. V. (2020). re-and post-launch emotions in new product development: Insights from twitter analytics of three products. International Journal of Information Management, 50, 111–127.
  11. WAHYUDI, M., & KRISTIYANTI, D. A. (2016). Sentiment analysis of smartphone product review using support vector machine algorithm- based particle swarm optimization. Journal of Theoretical & Ap- Plied Information Technology, 91, n.
  12. Yusuf, A. S., Razak, A., Hussin, C., & Busalim, A. H. (2018). Influence of e-WOM engagement on consumer purchase intention in social commerce. J. Serv. Market, 32 (4), 493–504