Sentiment Analysis of Customer Review Behavior in the Online Market toward Health Supplement Products
Article Sidebar
Main Article Content
บทคัดย่อ
This research analyzed customer sentiment through review behaviors towards health supplement products in the online market by utilizing natural language processing techniques. Review data were collected from 10 brands of health supplements, comprising 6,284 reviews in Thai, on the Shopee platform from July 2023 to March 2024. The analysis results revealed that positive sentiment scores higher than 0.6 clearly reflect customer satisfaction. K-Means and Fuzzy C-Means were employed for clustering models to enhance accuracy. To determine the optimal number of clusters, Elbow Method and Silhouette Score were utilized. Ultimately, it was found that customers were divided into two clusters (K=2), with key focus on words such as "efficacy," "product," "good," "taste," "consume," and "quality," which are critical factors influencing purchasing decisions. Furthermore, an evaluation of clustering quality using the K-Means and FCM models showed no significant differences. As indicated by the Silhouette Score (0.5319) and Davies-Bouldin Index (0.6991), both models held comparable clustering performance. This study offered important business implications, including customer segmentation, behavior analysis, marketing strategy development, and product/service improvement to help strengthen customer relationships and enhance competitiveness in the rapidly changing online market. Consequently, the findings of this research served as a vital tool for forecasting trends, developing effective strategies, and meeting future customer needs.
Article Details
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เอกสารอ้างอิง
Ahmed, W., Semary, N. A. E.-M., Amin, K. M. and Hammad, M. (2023). Sentiment analysis on Twitter using machine learning techniques and TF-IDF feature extraction: A comparative study. International Journal of Computers and Information, 10(3), 52-57.
Anderson, T., Sarkar, S. and Kelley, R. (2024). Analyzing public sentiment on sustainability: A comprehensive review and application of sentiment analysis techniques. Natural Language Processing Journal, 8(100097), 1-11.
Bharadwaj, L. (2023). Sentiment analysis in online product reviews: Mining customer opinions for sentiment classification. International Journal for Multidisciplinary Research, 5(5), 1-34.
Bomma, H. P. (2024). Natural language processing (NLP) in business intelligence. International Journal of Leading Research Publication, 5(9), 1-5
Bowornlertsutee, P. and Paireekreng, W. (2022). The model of sentiment analysis for classifyingthe online shopping reviews. Journal of Engineering and Digital Technology (JEDT), 10(1), 71-79.
Broklyn, P., Olukemi, A. and Bell, C. (2024). Social media sentiment analysis for brand reputation management. SSRN, 1-20.
Boonchote, T. (2022). Business intelligence insight. Retrieved April 29, 2024, from https://cathcarttechnology.co.th/insights/business-intelligence-insight/
Chanakot, B. and Phoksawat, K. (2024). Analysis of consumer sentiments towards online shopping using context-free grammar and deep learning. Revue d'Intelligence Artificielle, 38(1), 193-199.
Chen, Z., Hongthong, T. and Suthamdee, A. (2024). Strategies of language use and persuasion in supplementary product for beautiful skin advertisement. Journal of MCU Philosophy Review, 7(1), 214–229.
Dellarocas, C., Zhang, X. and Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive marketing, 21(4), 23-45.
GlobalWellnessInstitute. (2024). Understanding GWI’s mental wellness economy data. Global Wellness Institute. Retrieved June 18, 2024, from https://globalwellnessinstitute.org/global-wellness-institute-blog/2024/06/25/understanding-gwis-mental-wellness-economy-data-a-market-that-will-grow-from-181-billion-in-2022-to-330-billion-by-2027/
Gupta, H. and Patel, M. (2021). Method of text summarization using LSA and sentence-based topic modelling with BERT. In The International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 511–517). Coimbatore, India.
Gupta, P. and Jamwal, S. S. (2021). Designing and development of stemmer of Dogri using unsupervised learning. In N. Marriwala, C. C. Tripathi, S. Jain, & S. Mathapathi (Eds.), Soft computing for intelligent systems (pp. 147–156). Springer.
Kaburuana, E. R., Sari, Y. S. and Agustina, I. (2022). Sentiment analysis on product reviews from Shopee marketplace using the Naïve Bayes classifier. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, 13(3), 150-159.
Kanchanawiwin, A. (2023). Factors affecting consumer’s purchasing decision of dietary supplement: An empirical study on Generation Y. Master’s Independent study, Thammasat University, Bangkok.
Kulkarni, A. and Shivananda, A. (2019). Natural language processing recipes. India: Apress.
Lkotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B. and Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210.
Lukplamino. (2022). What is TF-IDF? Explained with the simplest example Medium. Retrieved June 18, 2024, from https://shorturl.asia/Lbxp6/
Lu, X., Ba, S., Huang, L. and Feng, Y. (2013). Promotional marketing or word-of-mouth? Evidence from online restaurant reviews. Information Systems Research, 24(3), 596-612.
Mao, Y., Liu, Q. and Zhang, Y. (2024). Sentiment analysis methods, applications, and challenges: A systematic literature review. Journal of King Saud University - Computer and Information Sciences, 36(4), 1-16.
Meeprasert, V. and Rattakan, E. (2021). An analysis of customer opinions on Shopee Thailand through Twitter. Journal of Information Systems in Business (JISB), 7(3), 6–18.
Mohamed, A. (2023). Sentiment score: A comprehensive guide. AIM Technologies. Retrieved April 7, 2024, from https://www.aimtechnologies.co/2023/09/
Nikhil.(2024). Guide to sentiment analysis using natural language processing. Retrieved April 26 2024, from https://www.analyticsvidhya.com/blog/2021/06/nlp-sentiment-analysis/
Nipa. (2024). Sentiment analysis: A marketing tool every business should know. Retrieved June 19, 2024, from https://nipa.co.th/th/article/digital-marketing/tracking-customer-journey-with-sentiment-analysis
Naiyanapakorn, P. (2023). Understanding online consumer behavior. Trade policy and strategy office. Retrieved April 24, 2024, from https://tpso.go.th/news/2402-0000000019
NoonInch. (2022). According to recent statistics on online product reviews for marketing purposes, 63% of consumers check Google Reviews before making a purchase decision. Everyday Marketing. Retrieved June 19, 2024, from https://www.everydaymarketing.co/trend-insight/stat-รีวิวสินค้า-online-for-marketing-plan/
Panyathorn, K. and Thajang, S. (2020). Opinions and consumption behaviors of supplementary food products among the elderly in Nongbu village, Sam Phrao Sub-district, Udon Thani Province. UdonThani Hospital Medical Journal, 28(2), 121–129.
Park, C. and Lee, T. M. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business research, 62(1), 61-67.
Packhai. (2023). 8 product review techniques for online stores to attract customers and boost purchase decisions. Retrieved April 18, 2024, from https://packhai.com/how-to-do-product-review/
Pichiyan, V., Muthulingam, S., Sathar, G., Nalajala, S., Akhil, C. and Das, M. N. (2023). Web scraping using natural language processing: Exploiting unstructured text for data extraction and analysis. Procedia Computer Science, 230, 193–202.
Pisinee. (2024). Sentiment analysis: When customer emotions can be understood through text. Retrieved October 26, 2024, from https://contentshifu.com/blog/what-is-sentiment-analysis
Rewnark, K. (2023). Factors affecting the decision making on purchasing dietary supplements through mobile applications. Master’s independent study, Thammasat University. Bangkok.
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F. and Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), 1-34.
Salman, Z. and Alomary, A. (2024). Performance of the K-means and fuzzy C-means algorithms in big data analytics. International Journal of Information Technology, 16(1), 465-470.
Shah, R. (2024). Different methods for calculating sentiment of text. Retrieved October 26, 2024, form https://www.analyticsvidhya.com/blog/2021/12/different-methods-for-calculating-sentiment-score-of-text/
Sirikul, C. and Samphanwattanachai, B. (2023). Factors affecting the purchase decision of sleep and stress support supplements among Thai consumers in Bangkok. Rajapark Journal, 17(53), 53–66.
SMEThailand. (2023). Online reviews as a powerful strategy: Which generation is most influenced by them?. Retrieved October 26, 2024, form https://www.smethailandclub.com/startup-digitalmarketing/8959.html
Stammback, D., Webersinke, N., Bingler, J. A., Kraus, M. and Leippold, M. (2022). A dataset for detecting real-world environmental claims. Center for Law and Economics Working Paper Series, 7(2022), (pp. 1-14). Gunupur, India.
Sukheja, S., Chopra, S. and Vijayalakshmi, M. (2020). Sentiment analysis using deep learning – A survey. In The International Conference on Computer Science, Engineering and Applications (pp. 1-4). Gunupur, India.
Sulianta, F., Ulfah, K. and Amalia, E. (2024). Revealing consumer preferences in the fashion industry using K-means clustering. International Journal of Engineering Continuity, 3(2), 34-53.
Tamanna. (2023). Why embeddings usually outperform TF-IDF: Exploring the power of NLP. Retrieved October 30, 2024, form https://medium.com/@tam.tamanna18/exploring-the-power-of-nlp-why-embeddings-usually-outperform-tf-idf-98742e7b0bce/
Thanam, Y., Sirigonganjana, V., Thanam, P., Panomratanasak, J. and Chulakasate, S. (2023). The consumer attitudes and preferences of gold N health supplements. Journal of Roi Kaensarn Academi, 8(12), 370–386.
Thansettakij. (2023). Global spotlight: 11 key health market segments projected to reach USD 1.1 trillion in 2023. Retrieved October 30, 2024, form https://www.thansettakij.com/business/marketing/581011/
Thakur, P. S., Verma, R. K. and Tiwari, R. (2024). Analysis of time complexity of K-means and fuzzy C-means clustering algorithm. Engineering Mathematics Letters, 4(2024), 1-9.
TKPon. (2020). Customer reviews. Takra online. Retrieved October 3, 2024, form https:// shorturl.asia/jnq29
Wen, Y., Liang, Y. and Zhu, X. (2023). Sentiment analysis of hotel online reviews using the BERT model and ERNIE model data from China. PLoS ONE, 18(3),1-14.
Wharton, B. (2024). Natural language processing: Transforming large data into strategic business insights. Retrieved April 26, 2024, from https://inmoment.com/blog/nlp-natural-language-processing/
Wisesight. (2023). What is sentiment analysis and how it enhances customer understanding and business performance. Retrieved October 30, 2024, form https://wisesight.com/news/what-is-sentiment-analysis/
Yadav, A. and Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 53(6), 4335-4385.
Zaghloul, M., Barakat, S. and Rezk, A. (2024). Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches. Journal of Retailing and Consumer Services, 79, 103865.
Zhu, Y. and Liang, G. (2024). Designing product upgrades in the presence of online consumer reviews. Managerial and Decision Economics, 45(6), 3915–3928.
