Cluster Analysis of Japanese Whiskey Product Review Using K-Means Clustering

Deden Witarsyah - Telkom University, Jalan Telekomunikasi, Bandung, 40257, Indonesia
Moh Adli Akbar - Telkom University, Jalan Telekomunikasi, Bandung, 40257, Indonesia
Villy Satria Praditha - Telkom University, Jalan Telekomunikasi, Bandung, 40257, Indonesia
Maria Sugiat - Telkom University, Jalan Telekomunikasi, Bandung, 40257, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.1.2601

Abstract


Since 2008, the Japanese whiskey business has grown steadily. Overall, the whiskey market (at factory price) is expected to reach $2.95 billion in 2019, accounting for 8.6 percent of the entire alcoholic beverage industry. The rise in popularity of Japanese whiskey is associated with the country's growing international reputation. Founded 1985 as an independent bottler, Master of Malt was the first company to service clients who ordered single malt whiskey through the mail-order system. Master of Malt's omnichannel approach encompasses all channels available to the company. Known as their 'omnichannel,' this refers to the organization's capability to provide speed and precision from any place at any time. As their brand has grown over the years, they have used various marketing strategies, including a website redesign and rebuild that involved the creation of all relevant content and designing and constructing landing pages for their website. Following a clustering technique, we discovered that the data is being divided into four distinct groups and that these clusters may serve as a recommender system based on the occurrence of terms in each of the categories. Our summarizing component combined phrases related to the exact subtopics and provided users with a concise summary and sentimental information about the group of phrases.

Keywords


K-Means Clustering; Japanese Whiskey; Omnichannel

Full Text:

PDF

References


M. Otsuka, "Market Overview - Whiskey is Up in Japan," 2020. [Online]. Available: https://www.fas.usda.gov/data/japan-market-overview-whiskey-japan

A. Mosquera, C. Olarte Pascual, and E. Juaneda Ayensa, “Understanding the customer experience in the age of omni-channel shopping,” Revista ICONO14 Revista científica de Comunicación y Tecnologías emergentes, vol. 15, no. 2, pp. 92–114, Jul. 2017, doi: 10.7195/ri14.v15i2.1070.

A. Yadav, A. Patel, and M. Shah, "A comprehensive review on resolving ambiguities in natural language processing," AI Open, vol. 2, pp. 85–92, Jan. 2021, doi: 10.1016/J.AIOPEN.2021.05.001.

N. M. N. Mathivanan, N. A. Md. Ghani, and R. M. Janor, "Analysis of K-Means Clustering Algorithm: A Case Study Using Large Scale E-Commerce Products," in 2019 IEEE Conference on Big Data and Analytics (ICBDA), 2019, pp. 1–4. doi: 10.1109/ICBDA47563.2019.8987140.

N. Garg and R. Rani, "Analysis and visualization of Twitter data using k-means clustering," in 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 2017, pp. 670–675. doi: 10.1109/ICCONS.2017.8250547.

R. Liang, Y. Li, X. Chen, and J. Chen, "Patent Trend Analysis through Text Clustering based on K-Means Algorithm," in 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), 2020, pp. 115–118. doi: 10.1109/ISCEIC51027.2020.00032.

H. S. González, I. P. López, P. G. Bringas, H. Quintián, and E. Corchado, “16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021),” 2021, vol. 1401. doi: 10.1007/978-3-030-87869-6.

J. S. Saltz, "CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps," in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 2337–2344. doi: 10.1109/BigData52589.2021.9671634.

J. Han, M. Kamber, and J. Pei, “Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems)," 2011.

Vishal, A. Kumar, and Dr. P. Saini, "Use of K-Means Clustering Method for Books Data in Acharya Raghuveer Library, Central University of Himachal Pradesh, Dharamshala, India," Library Philosophy and Practice (e-journal), Nov. 2021, Accessed: Jan. 26, 2022. [Online]. Available: https://digitalcommons.unl.edu/libphilprac/6655

S. P. Tamba, M. D. Batubara, W. Purba, M. Sihombing, V. M. Mulia Siregar, and J. Banjarnahor, "Book data grouping in libraries using the k-means clustering method," Journal of Physics: Conference Series, vol. 1230, no. 1, p. 012074, 2019, doi: 10.1088/1742-6596/1230/1/012074.

M. E. CELEBI and H. A. KINGRAVI, "DETERMINISTIC INITIALIZATION OF THE K-MEANS ALGORITHM USING HIERARCHICAL CLUSTERING," International Journal of Pattern Recognition and Artificial Intelligence, vol. 26, no. 07, p. 1250018, Nov. 2012, doi: 10.1142/S0218001412500188.

J. Gola et al., "Advanced microstructure classification by data mining methods," Computational Materials Science, vol. 148, pp. 324–335, Jun. 2018, doi: 10.1016/J.COMMATSCI.2018.03.004.

J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," in Data Mining: Concepts and Techniques (Third Edition), vol. 3, J. Han, M. Kamber, and J. Pei, Eds. Boston: Morgan Kaufmann, 2012, pp. 443–493. doi: https://doi.org/10.1016/B978-0-12-381479-1.00001-0.

C. Sammut and G. I. Webb, Eds., "TF–IDF," in Encyclopedia of Machine Learning, Boston, MA: Springer US, 2010, pp. 986–987. doi: 10.1007/978-0-387-30164-8_832.

A. Larasati, M. Farhan, P. Rahmawati, J. Sayono, A. Purnomo, and E. Mohamad, "Exploring the relationship among polarity, subjectivity, and clusters characteristics of visitor review on tourist destination in Malang, Indonesia," in Community Empowerment through Research, Innovation and Open Access, Routledge, 2021, pp. 22–27. doi: 10.1201/9781003189206-5.

M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter," Oct. 2019. doi: 10.1109/ICIC47613.2019.8985884.

M. Darwis, G. Tri Pranoto, and Y. Eka Wicaksana, "Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter," 2020.

R. Nainggolan and E. Purba, "Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering," Journal of Computing and Applied Informatics (JoCAI), vol. 4, no. 2, p. 121, 2020, doi: 10.32734/jocai.v4.i2-2855.

K. Orkphol and W. Yang, "Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony," International Journal of Computational Intelligence and Applications, vol. 18, no. 3, Sep. 2019, doi: 10.1142/S1469026819500172.